Land surface temperature (LST) is a phenomenon that significantly affects the environment, the cities’ liveability, and the citizens’ well-being. This Study aims to perform a comparative study of the microclimate an...Land surface temperature (LST) is a phenomenon that significantly affects the environment, the cities’ liveability, and the citizens’ well-being. This Study aims to perform a comparative study of the microclimate and Surface Urban Heat Island (SUHI) phenomenon of two metropolitan cities of India, i.e. Jaipur and Ahmedabad, using MODIS Satellite data, whereas Landsat Data was used to analyse the Land Surface Characteristics by an index-based approach. The Study’s findings reveal that Ahmedabad has 35.53 per cent of the total area classified as having a low potential, and 13.55 per cent is designated as a high potential LST zone. Meanwhile, in Jaipur, 30.45 per cent of the city’s total area is identified as a low potential LST zone and 12.69 per cent as a high potential LST zone. This Study highlights the importance of mitigating the UHI phenomenon in urban centres for the overall well-being of city dwellers. It will help policymakers and stakeholders comprehend plans and take initiatives to minimise the effects of the UHI phenomenon on rapidly growing cities. .展开更多
The automatic and accurate classification of Magnetic Resonance Imaging(MRI)radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy.Since the majority of MRI radiology reports are u...The automatic and accurate classification of Magnetic Resonance Imaging(MRI)radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy.Since the majority of MRI radiology reports are unstructured,the manual information extraction is time-consuming and requires specific expertise.In this paper,a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically.This method combines the Natural Language Processing technique and statisticalMachine Learning methods.122 realMRI radiology text reports(97 epilepsy,25 non-epilepsy)are studied by our proposed method which consists of the following steps:(i)for a given text report our systems first cleans HTML/XML tags,tokenize,erase punctuation,normalize text,(ii)then it converts into MRI text reports numeric sequences by using indexbased word encoding,(iii)then we applied the deep learning models that are uni-directional long short-term memory(LSTM)network,bidirectional long short-term memory(BiLSTM)network and convolutional neural network(CNN)for the classifying comparison of the data,(iv)finally,we used 70%of used for training,15%for validation,and 15%for test observations.Unlike previous methods,this study encompasses the following objectives:(a)to extract significant text features from radiologic reports of epilepsy disease;(b)to ensure successful classifying accuracy performance to enhance epilepsy data attributes.Therefore,our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models.The traditionalmethod is numeric sequences by using index-based word encoding which has been made for the first time in the literature,is successful feature descriptor in the epilepsy data set.The BiLSTM network has shown a promising performance regarding the accuracy rates.We show that the larger sizedmedical text reports can be analyzed by our proposed method.展开更多
文摘Land surface temperature (LST) is a phenomenon that significantly affects the environment, the cities’ liveability, and the citizens’ well-being. This Study aims to perform a comparative study of the microclimate and Surface Urban Heat Island (SUHI) phenomenon of two metropolitan cities of India, i.e. Jaipur and Ahmedabad, using MODIS Satellite data, whereas Landsat Data was used to analyse the Land Surface Characteristics by an index-based approach. The Study’s findings reveal that Ahmedabad has 35.53 per cent of the total area classified as having a low potential, and 13.55 per cent is designated as a high potential LST zone. Meanwhile, in Jaipur, 30.45 per cent of the city’s total area is identified as a low potential LST zone and 12.69 per cent as a high potential LST zone. This Study highlights the importance of mitigating the UHI phenomenon in urban centres for the overall well-being of city dwellers. It will help policymakers and stakeholders comprehend plans and take initiatives to minimise the effects of the UHI phenomenon on rapidly growing cities. .
文摘The automatic and accurate classification of Magnetic Resonance Imaging(MRI)radiology report is essential for the analysis and interpretation epilepsy and non-epilepsy.Since the majority of MRI radiology reports are unstructured,the manual information extraction is time-consuming and requires specific expertise.In this paper,a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically.This method combines the Natural Language Processing technique and statisticalMachine Learning methods.122 realMRI radiology text reports(97 epilepsy,25 non-epilepsy)are studied by our proposed method which consists of the following steps:(i)for a given text report our systems first cleans HTML/XML tags,tokenize,erase punctuation,normalize text,(ii)then it converts into MRI text reports numeric sequences by using indexbased word encoding,(iii)then we applied the deep learning models that are uni-directional long short-term memory(LSTM)network,bidirectional long short-term memory(BiLSTM)network and convolutional neural network(CNN)for the classifying comparison of the data,(iv)finally,we used 70%of used for training,15%for validation,and 15%for test observations.Unlike previous methods,this study encompasses the following objectives:(a)to extract significant text features from radiologic reports of epilepsy disease;(b)to ensure successful classifying accuracy performance to enhance epilepsy data attributes.Therefore,our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models.The traditionalmethod is numeric sequences by using index-based word encoding which has been made for the first time in the literature,is successful feature descriptor in the epilepsy data set.The BiLSTM network has shown a promising performance regarding the accuracy rates.We show that the larger sizedmedical text reports can be analyzed by our proposed method.
