The significance of land use classification has garnered attention due to its implications for climate and ecosystems.This paper establishes a connection by introducing and applying automatic machine learning(Auto ML)...The significance of land use classification has garnered attention due to its implications for climate and ecosystems.This paper establishes a connection by introducing and applying automatic machine learning(Auto ML)techniques to salt lake landscape,with a specific focus on the Qarhan Salt Lake area.Utilizing Landsat-5 Thematic Mappe(TM)and Landsat-8 Operational Land Imager(OLI)imagery,six machine learning algorithms were employed to classify eight land use types from 2000 to 2020.Results show that XGBLD performed optimally with 77%accuracy.Over two decades,salt fields,construction land,and water areas increased due to transformations in saline land and salt flats.The exposed lakes area exhibited a rise followed by a decline,mainly transforming into salt flats.Agricultural land areas slightly increased,influenced by both human activities and climate.Our analysis reveals a strong correlation between salt fields and precipitation,while exposed lakes demonstrate a significant negative correlation with evaporation and temperature,highlighting their vulnerability to climate change.Additionally,human water usage was identified as a significant factor impacting land use change,emphasizing the dual influence of anthropogenic activities and natural factors.This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.展开更多
Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Design...Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Designed and Methods We conducted a retrospective diagnostic study using 1,959 audiogram images from patients aged 7 years and older at the Faculty of Medicine,Vajira Hospital,Navamindradhiraj University.We employed an object detection approach to digitize audiograms and developed multiple machine learning models to classify six hearing loss levels.The dataset was split into 70%training(1,407 images)and 30%testing(352 images)sets.We compared our model's performance with classifications based on manually extracted audiogram values and otolaryngologists'interpretations.Result Our object detection-based model achieved an F1-score of 94.72%in classifying hearing loss levels,comparable to the 96.43%F1-score obtained using manually extracted values.The Light Gradient Boosting Machine(LGBM)model is used as the classifier for the manually extracted data,which achieved top performance with 94.72%accuracy,94.72%f1-score,94.72 recall,and 94.72 precision.In object detection based model,The Random Forest Classifier(RFC)model showed the highest 96.43%accuracy in predicting hearing loss level,with a F1-score of 96.43%,recall of 96.43%,and precision of 96.45%.Conclusion Our proposed automated approach for audiogram digitization and hearing loss classification performs comparably to traditional methods and otolaryngologists'interpretations.This system can potentially assist otolaryngologists in providing more timely and effective treatment by quickly and accurately classifying hearing loss.展开更多
There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR）...There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.展开更多
基金supported cooperatively by the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0805)the National Natural Science Foundation of China(U20A2088)+1 种基金the Innovation Team Foundation of Qinghai Office of Science and Technology(2022-ZJ-903)CITIC Top 10 Technological Innovation Projects Comprehensive development and utilization of salt lake resources(2023ZXKYA05100).
文摘The significance of land use classification has garnered attention due to its implications for climate and ecosystems.This paper establishes a connection by introducing and applying automatic machine learning(Auto ML)techniques to salt lake landscape,with a specific focus on the Qarhan Salt Lake area.Utilizing Landsat-5 Thematic Mappe(TM)and Landsat-8 Operational Land Imager(OLI)imagery,six machine learning algorithms were employed to classify eight land use types from 2000 to 2020.Results show that XGBLD performed optimally with 77%accuracy.Over two decades,salt fields,construction land,and water areas increased due to transformations in saline land and salt flats.The exposed lakes area exhibited a rise followed by a decline,mainly transforming into salt flats.Agricultural land areas slightly increased,influenced by both human activities and climate.Our analysis reveals a strong correlation between salt fields and precipitation,while exposed lakes demonstrate a significant negative correlation with evaporation and temperature,highlighting their vulnerability to climate change.Additionally,human water usage was identified as a significant factor impacting land use change,emphasizing the dual influence of anthropogenic activities and natural factors.This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.
文摘Objective To develop and evaluate an automated system for digitizing audiograms,classifying hearing loss levels,and comparing their performance with traditional methods and otolaryngologists'interpretations.Designed and Methods We conducted a retrospective diagnostic study using 1,959 audiogram images from patients aged 7 years and older at the Faculty of Medicine,Vajira Hospital,Navamindradhiraj University.We employed an object detection approach to digitize audiograms and developed multiple machine learning models to classify six hearing loss levels.The dataset was split into 70%training(1,407 images)and 30%testing(352 images)sets.We compared our model's performance with classifications based on manually extracted audiogram values and otolaryngologists'interpretations.Result Our object detection-based model achieved an F1-score of 94.72%in classifying hearing loss levels,comparable to the 96.43%F1-score obtained using manually extracted values.The Light Gradient Boosting Machine(LGBM)model is used as the classifier for the manually extracted data,which achieved top performance with 94.72%accuracy,94.72%f1-score,94.72 recall,and 94.72 precision.In object detection based model,The Random Forest Classifier(RFC)model showed the highest 96.43%accuracy in predicting hearing loss level,with a F1-score of 96.43%,recall of 96.43%,and precision of 96.45%.Conclusion Our proposed automated approach for audiogram digitization and hearing loss classification performs comparably to traditional methods and otolaryngologists'interpretations.This system can potentially assist otolaryngologists in providing more timely and effective treatment by quickly and accurately classifying hearing loss.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2014 ZX03001027)
文摘There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.
