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Mapping of cropland,cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

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Geo-Spatial Information Science 2023年第3期26卷 302-320页

作者： Aqil Tariq Jianguo Yan Alexandre S.Gagnon Mobushir Riaz Khan Faisal Mumtaz State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Biological and Environmental Sciences Liverpool John Moores UniversityLiverpoolUK School of Environmental Science Charles Sturt UniversityAlburyAustralia State Key Laboratory of Remote Sensing Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences(UCAS) BeijingChina

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely ***,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral *** study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study *** methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a decision tree classifier(DTC)and a Random Forest(RF)*** best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML *** methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping *** results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county *** results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

关键词： Sentinel-2 Random Forest cropland crop types cropping patterns decision tree classifier

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Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and decision tree Algorithm

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International Journal of Automation and computing 2020年第4期17卷 551-561页

作者： Santanu Sahoo Asit Subudhi Manasa Dash Sukanta Sabut Department of Electronics and Communication Engineering ITERSOA UniversityBhubaneswar 751030India Department of Mathematics Silicon Institute of TechnologyBhubaneswar 751024India School of Electronics Engineering KIIT deemed to be UniversityBhubaneswar 751024India

Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)*** a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality *** this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG *** experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia *** feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR *** total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia *** proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%*** accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 *** the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to *** multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.

关键词： Electrocardiogram(ECG) cardiac arrhythmias empirical mode decomposition(EMD) variational mode decomposition(VMD) hybrid features decision tree classifier

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Self-Care Assessment for Daily Living Using Machine Learning Mechanism

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Computers, Materials & Continua 2022年第7期72卷 1747-1764页

作者： Mouazma Batool Yazeed Yasin Ghadi Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer *** and optical sensors are widely used to assess the daily living activities in healthy people and people with certain *** conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity *** this paper,an RGB-D-based ADL recognition system has been ***,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a *** on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)***,random forest classifier has been used to classify activities into different *** n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.

关键词： Angular geometric features decision tree classifier human activity recognition probability based incremental learning ridge detection

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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Computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the decision tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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