Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis...Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.Methods:This study introduces a data acquisition technique and a preprocessing pipeline,combined with multivariate long short-term memory(M-LSTM)and AdaBoost models.These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients,using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.Results:The methodology proposed in this study significantly improved performance metrics.The testing accuracy reached 80%with the AdaBoost model,while the M-LSTM model achieved an accuracy of 82%.This represents a 20%increase in accuracy compared to a recent similar study.Discussion:The findings indicate that the multivariate model,specifically the M-LSTM,is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.展开更多
The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important pa...The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. The model loss is less than 0.2. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The proposed model has high applicability and accuracy for flowering forecast. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.展开更多
Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic pa...Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic parameter for efficient online in-vehicle energy management.However,vehicles’speed forecasting is a challenging task,because its estimation is closely related to various features,which can be classified into two categories,endogenous and exogenous features.Endogenous features represent electric vehicles’characteristics,whereas exogenous ones represent its surrounding context,such as traffic,weather,and road conditions.In this paper,a speed forecasting method based on the Long Short-Term Memory(LSTM)is introduced.The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries.The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting.Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.展开更多
文摘Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.Methods:This study introduces a data acquisition technique and a preprocessing pipeline,combined with multivariate long short-term memory(M-LSTM)and AdaBoost models.These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients,using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.Results:The methodology proposed in this study significantly improved performance metrics.The testing accuracy reached 80%with the AdaBoost model,while the M-LSTM model achieved an accuracy of 82%.This represents a 20%increase in accuracy compared to a recent similar study.Discussion:The findings indicate that the multivariate model,specifically the M-LSTM,is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.
文摘The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. The model loss is less than 0.2. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The proposed model has high applicability and accuracy for flowering forecast. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.
基金supported by MIGRID project(No.5-398,2017–2019),which was funded by USAID under the PEER program
文摘Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic parameter for efficient online in-vehicle energy management.However,vehicles’speed forecasting is a challenging task,because its estimation is closely related to various features,which can be classified into two categories,endogenous and exogenous features.Endogenous features represent electric vehicles’characteristics,whereas exogenous ones represent its surrounding context,such as traffic,weather,and road conditions.In this paper,a speed forecasting method based on the Long Short-Term Memory(LSTM)is introduced.The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries.The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting.Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.
