Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for th...Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for the future.Maintaining crop production requires early diagnosis.Notably,Coffee Leaf Miner(CLM)Machine learning(ML)offers promising tools for automated disease detection.Early detection of CLM is crucial for minimising yield losses.However,this study explores the effectiveness of using Convolutional Neural Networks(CNNs)with transfer learning algorithms ResNet50,DenseNet121,MobileNet,Inception,and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected.Leveraging the JMuBEN1 dataset,the proposed hybrid VGG19 model achieved exceptional performance,reaching 97%accuracy on both training and validation data.Additionally,high scores for precision,recall,and F1-score.The confusion matrix shows that all the test samples were correctly classified,which indicates the model’s strong performance on this dataset,demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves.This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management,and adapting it for practical deployment in agricultural settings.As well as supporting farmers in detecting diseases using modern,inexpensive methods that do not require specialists,and utilising deep learning technologies.展开更多
Background Patients who experience acute hospitalization face a risk of suffering adverse events,such as delir-ium,pressure ulcers,or pain.This risk gets aggravated in individuals with multimorbidity.Furthermore,the p...Background Patients who experience acute hospitalization face a risk of suffering adverse events,such as delir-ium,pressure ulcers,or pain.This risk gets aggravated in individuals with multimorbidity.Furthermore,the prevalence of multimorbidity is notably high,and gets even higher for elder people.In addition,the interaction between multiple adverse events can significantly impact mortality.Previous efforts to predict this kind of events have not produced satisfactory results,particularly for older patients with multimorbidity in emergency room settings.Having a clinical prediction rule(CPR)that can accurately predict adverse events in this population is crucial to prevent these events and improve patient outcomes.Methods This study enrolled patients with multimorbidity who were admitted to an acute care unit from De-cember 2021 to June 2023.The dimensionality of this dataset was reduced from 43 to 10 features through the implementation of a normalization-based ensemble technique,integrating feature selection methods from differ-ent categories:filter methods,wrapper methods,and embedded models to ensure robust validation.A stratified k-fold cross-validation was applied to reduce the risk of overfitting caused by the imbalanced distribution of the data set.Once the relevant predictors were identified,the sequential forward selection(SFS)technique was used to determine the optimal subsets of predictors that maximize model accuracy.Results The evaluation of the performance of these subsets using different classification algorithms led to the de-velopment of a CPR using only the three most relevant predictors.The metrics of different models were compared,and the support vector machine(SVM)model was selected due to its superior area under curve(AUC)-receiver operator characteristic(ROC)(0.93)and better handling of class unbalancing and rest of parameters(accuracy 0.91,precision and recall 0.83,and specificity 0.94).To facilitate the application of this prediction rule,a web application that streamlines the detection,classification,and prediction processes of these outcomes was devel-oped.Conclusion The proposed model may achieve high accuracy and stability by requiring fever events to predictad-verse outcomes in patients with multimorbidity in emergency settings compared with conventional methods.展开更多
The smart grid concept is key to the energy revolution that has been taking place in recent years.Smart Grids have been present in energy research since their emergence.However,the scarcity of data from different ener...The smart grid concept is key to the energy revolution that has been taking place in recent years.Smart Grids have been present in energy research since their emergence.However,the scarcity of data from different energy sources,hardware power,or co-simulation environments has hindered their development.With advances in multi-agent-based systems,the possibility of simulating the behavior of different energy sources,combining real building consumption,and simulated data,storage batteries and vehicle charging points,has opened up.This development has resulted in much research published using both simulated and physical data.All these investigations show that the main problem is that the machine learning algorithms do not fully match the real behavior,it is complex to use them to replicate the different actions to be performed.This paper aims to combine the approach of behavior prediction with state-of-the-art techniques,such as deep learning and deep reinforcement learning,to simulate unknown or critical system scenarios.A very important element in smart grids is the possibility of maintaining consumption within specific ranges(flexibility).For this purpose,we have made use of Tensorflow libraries that predict energy consumption and deep reinforcement learning to select the optimal actions to be performed in our system.The developed platform is flexible enough to include new technologies such as smart batteries,electric vehicles,etc.,and it is oriented to real-time operation,being applied in an on-going real project such as the European ebalance-plus project.展开更多
文摘Coffee is an important agricultural commodity,and its production is threatened by various diseases.It is also a source of concern for coffee-exporting countries,which is causing them to rethink their strategies for the future.Maintaining crop production requires early diagnosis.Notably,Coffee Leaf Miner(CLM)Machine learning(ML)offers promising tools for automated disease detection.Early detection of CLM is crucial for minimising yield losses.However,this study explores the effectiveness of using Convolutional Neural Networks(CNNs)with transfer learning algorithms ResNet50,DenseNet121,MobileNet,Inception,and hybrid VGG19 for classifying coffee leaf images as healthy or CLM-infected.Leveraging the JMuBEN1 dataset,the proposed hybrid VGG19 model achieved exceptional performance,reaching 97%accuracy on both training and validation data.Additionally,high scores for precision,recall,and F1-score.The confusion matrix shows that all the test samples were correctly classified,which indicates the model’s strong performance on this dataset,demonstrating that the model is effective in distinguishing between healthy and CLM-infected leaves.This suggests strong potential for implementing this approach in real-world coffee plantations for early disease detection and improved disease management,and adapting it for practical deployment in agricultural settings.As well as supporting farmers in detecting diseases using modern,inexpensive methods that do not require specialists,and utilising deep learning technologies.
基金supported by projects Including people in smart city applications(PID2021-125527NB-I00)funded by the Spanish Ministry of Science and Innovation,and project Desarrollo de una regla de predicción clínica para eventos adversos en pacientes con multimorbilidad que ingresan en hospitalización aguda y Urgencias.Estudio PREDI-CUIDD-HU(PI-0107-2020)funded by the Public Healthcare System(Andalusian Regional Ministry of Health and Families).
文摘Background Patients who experience acute hospitalization face a risk of suffering adverse events,such as delir-ium,pressure ulcers,or pain.This risk gets aggravated in individuals with multimorbidity.Furthermore,the prevalence of multimorbidity is notably high,and gets even higher for elder people.In addition,the interaction between multiple adverse events can significantly impact mortality.Previous efforts to predict this kind of events have not produced satisfactory results,particularly for older patients with multimorbidity in emergency room settings.Having a clinical prediction rule(CPR)that can accurately predict adverse events in this population is crucial to prevent these events and improve patient outcomes.Methods This study enrolled patients with multimorbidity who were admitted to an acute care unit from De-cember 2021 to June 2023.The dimensionality of this dataset was reduced from 43 to 10 features through the implementation of a normalization-based ensemble technique,integrating feature selection methods from differ-ent categories:filter methods,wrapper methods,and embedded models to ensure robust validation.A stratified k-fold cross-validation was applied to reduce the risk of overfitting caused by the imbalanced distribution of the data set.Once the relevant predictors were identified,the sequential forward selection(SFS)technique was used to determine the optimal subsets of predictors that maximize model accuracy.Results The evaluation of the performance of these subsets using different classification algorithms led to the de-velopment of a CPR using only the three most relevant predictors.The metrics of different models were compared,and the support vector machine(SVM)model was selected due to its superior area under curve(AUC)-receiver operator characteristic(ROC)(0.93)and better handling of class unbalancing and rest of parameters(accuracy 0.91,precision and recall 0.83,and specificity 0.94).To facilitate the application of this prediction rule,a web application that streamlines the detection,classification,and prediction processes of these outcomes was devel-oped.Conclusion The proposed model may achieve high accuracy and stability by requiring fever events to predictad-verse outcomes in patients with multimorbidity in emergency settings compared with conventional methods.
文摘The smart grid concept is key to the energy revolution that has been taking place in recent years.Smart Grids have been present in energy research since their emergence.However,the scarcity of data from different energy sources,hardware power,or co-simulation environments has hindered their development.With advances in multi-agent-based systems,the possibility of simulating the behavior of different energy sources,combining real building consumption,and simulated data,storage batteries and vehicle charging points,has opened up.This development has resulted in much research published using both simulated and physical data.All these investigations show that the main problem is that the machine learning algorithms do not fully match the real behavior,it is complex to use them to replicate the different actions to be performed.This paper aims to combine the approach of behavior prediction with state-of-the-art techniques,such as deep learning and deep reinforcement learning,to simulate unknown or critical system scenarios.A very important element in smart grids is the possibility of maintaining consumption within specific ranges(flexibility).For this purpose,we have made use of Tensorflow libraries that predict energy consumption and deep reinforcement learning to select the optimal actions to be performed in our system.The developed platform is flexible enough to include new technologies such as smart batteries,electric vehicles,etc.,and it is oriented to real-time operation,being applied in an on-going real project such as the European ebalance-plus project.
