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Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury

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EUROPEAN SPINE JOURNAL 2023年第11期32卷 3825-3835页

作者： Li, Meng-Pan Liu, Wen-Cai Wu, Jia-Bao Luo, Kun Liu, Yu Zhang, Yu Xiao, Shi-Ning Liu, Zhi-Li Huang, Shan-Hu Liu, Jia-Ming Nanchang Univ Affiliated Hosp 1 Dept Orthoped Surg 17 Yongwaizheng St Nanchang 330006 Jiangxi Peoples R China Nanchang Univ Clin Med Coll 1 Nanchang 330006 Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Dept Orthopaed Shanghai Peoples R China Nanchang Univ Inst Spine & Spinal Cord 17 Yongwaizheng St Nanchang 330006 Jiangxi Peoples R China

PurposeThe purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment *** with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the *** total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on rf algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656).ConclusionAge, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on rf algorithm had the best performance.

关键词： Pulmonary infection SCI Machine learning rf algorithm Complication

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Determining the most appropriate drought index using the random forest algorithm with an emphasis on agricultural drought

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NATURAL HAZARDS 2023年第1期115卷 923-946页

作者： Zarei, Abdol Rassoul Mahmoudi, Mohammad Reza Moghimi, Mohammad Mehdi Fasa Univ Dept Range & watershed management Nat Engn Fac Agr Fasa Iran Fasa Univ Dept Stat Fac Sci Fasa Iran Fasa Univ Dept water Sci Engn Coll Agr Fasa Iran

Since drought can mainly affect agriculture, especially rain-fed agriculture with high dependence on rainwater, and consequently jeopardize food security and social protection, it is absolutely essential to determine an appropriate drought index to assess the agricultural drought more accurately. In this paper, the random forest algorithm (rf) was employed to compare the capabilities of six more commonly used drought indices in agricultural drought assessment, namely standardized precipitation evapotranspiration index (SPEI), modified SPEI (M-SPEI), reconnaissance drought index (RDI), modified RDI (M-RDI), standardized precipitation index (SPI) and modified SPI (M-SPI) based on the correlation between the drought indices (i.e., independent variables) and yield of rain-fed barley (i.e., response variable). In this study, the data series of 10 stations with appropriate climate variety from 1968 to 2017 were used on 1-, 3-, 6-, and 12-month time scales (27 time periods in total). The results indicated that the linear regression between the simulated and predicted yield of rain-fed barley using the AquaCrop model and the rf algorithm (respectively) had no difference with a perfect reliable line (Y = X) in 0.05 or 0.01 significant levels. The R-2 between simulated and predicted barley yield (YB) was also significant at 0.01 levels in all stations, indicating the appropriate capability of the rf algorithm in YB prediction. The results also showed that some indices had better outcomes than others in stations;for example, the SPEI index in Arak, Mashhad, Shahre Kord, and Shiraz stations, the M-SPEI index in Babolsar, Gazvin, and Ramsar, the SPEI and M-SPEI indices in Esfahan and Gorgan stations, and the RDI and SPI indices in Rasht stations had the best outcome. Finally, it was recommended to use the SPEI and M-SPEI indices for agricultural drought assessment.

关键词： rf algorithm Drought indices Yield Rain-fed barley Agricultural drought

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Detection of spectrum hole from n-number of primary users using machine learning algorithms

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JOURNAL OF ENGINEERING-JOE 2020年第5期2020卷 175-188页

作者： Venkateshkumar, Udayamoorthy Ramakrishnan, Srinivansan Sri Krishna Coll Technol Dept ECE Coimbatore 641042 Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept IT Pollachi 642003 Tamil Nadu India

A method for detecting spectrum holes based on the n-number of primary users (PU's) in a cognitive radio environment, using a cooperative spectrum sensing model is proposed in this study. The fusion centre, senses the n-number of PUs. When the number of PUs is >200, the probability of detection decreases, while the probability of a false alarm increases. The authors use the random forest (rf) algorithm to classify a customised dataset of 600 training samples. Further, they compare the rf algorithm and the k-means clustering algorithm, using test datasets with a minimum of ten PUs and a maximum of 500 PUs. Five different signal features are considered as the attributes in the proposed model. The maximum probability of detection is achieved using the k-means clustering algorithm in the case of 200 PUs and is 99.17%, while the false alarm probability is 0.8%. The receiver operating characteristic curves indicated that probability of detecting a spectrum hole in the case of the dataset with 500 PUs is 97.67% with the signal to noise ratio ranging from 10 to -12 dB. The accuracy can be increased if the number of clusters formed is increased, depending on the number of test samples.

关键词： cognitive radio signal detection radio spectrum management probability learning (artificial intelligence) spectrum hole primary users machine learning algorithms PU cognitive radio environment spectrum sensing model detection decreases false alarm increases random forest algorithm rf algorithm clustering algorithm 500 PUs maximum probability 200 PUs false alarm probability noise figure -12 0 dB to 10 0 dB

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A Comparative Analysis of Machine Learning Techniques for Breast Cancer Prediction 2

A Comparative Analysis of Machine Learning Techniques for Br...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dutta, Umesh Kaushik, Simran Iyer, Srinidhi Singh, Ina Manav Rachna International Institute of Research and Studies Department of Electronics and Communication Engineering Haryana Faridabad India Manav Rachna International Institute of Research and Studies Manav Rachna Innovation & Incubation Centre Haryana Faridabad India

ISBN: (纸本)9798350327533

One of the major diseases found in women worldwide is Breast Cancer. More than 2.2M cases have been reported with about a 30% mortality rate in accordance with WHO. The rapid advancement of technology has led to breakthroughs in different fields such as medicine, automotive, etc. Despite advancements in screening technologies, achieving high sensitivity and specificity for early detection remains a challenge. Machine Learning is one of the front runners and is currently playing a vital role in solving real-world problems, particularly in the healthcare domain. Using different Machine Learning algorithms prediction of Breast Cancer has been tallied in this work. Various algorithms such as SVM (Support Vector Machine), KNN (K-Nearest Neighbours), DT (Decision Tree), and rf (Random Forest) have vast application areas. In this study, these algorithms have been tested and evaluated on the basis of precision, recall, and accuracy which are the key performance metrics for the evaluation of the model. The dataset for breast Cancer has been pulled from Kaggle, which is the most prominently used dataset by researchers. The simulation results revealed that Random Forest outperformed the others with the precision of 94.5%, recall of 94%, and accuracy of 95%, clearly showing its potential to be used for early prediction of Breast Cancer. © 2024 IEEE.

关键词： Breast Cancer Prediction DT algorithm KNN algorithm Modelling using ML rf algorithm SVM algorithm

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Revolutionizing Agriculture through Leaf Disease Prediction in Plants

Revolutionizing Agriculture through Leaf Disease Prediction ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Anand, Sahana Shri Sankaran, Sariga Ravi, Sandhiya Hariharan, Shanmugasundaram Kukreja, Vinay Vasantha, S.V. Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Cse Hyderabad India Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350308167

India's agricultural strength is hindered by plant diseases due to limited understanding of disease markers and remedies. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), offers hope for enhanced disease detection in agriculture. Our proposal centers on CNNs for accurate plant disease identification, streamlining the process. Urgency arises from economic repercussions and global food security. Deep learning is employed in various sectors such as healthcare, finance, and agriculture. Its applications range from identifying diseases, classifying weeds and leaves, to predicting soil moisture levels. The survey tackles current trends and objections in deep learning for plant disease prediction, guiding aspiring researchers. Machine Learning (ML) and Deep Learning (DL) tools replace costly visual inspection. Our study deploys ML algorithms including K Nearest Neighbors (KNN), Logistic Regression, Random Forest(rf) algorithm, Naïve Bayes, Decision Trees, and Support Vector Machines(SVM) on a diverse crop image dataset. A CNN-based system, coupled with ML soil analysis, suggests fitting crops, enabling informed planting choices. Classifier comparison validates Support Vector Classifier's (SVC) superiority. Swift disease detection is critical for food demand and economic growth. This study proposes a modern evaluation system using ML and pre-trained models (VGG16, InceptionV3, ResNet152) on datasets to classify plant diseases. IoT further augments disease and yield prediction, vital for modern agriculture. © 2023 IEEE.

关键词： Artificial Intelligence(AI) CNN Deep learning Internet Of Things (IOT) KNN Logistic Regression Machine learning pre-trained models rf algorithm RNN SVM swift disease diagnosis

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Correction to a Simple Biosphere Model 2 (SiB2) Simulation of Energy and Carbon Dioxide Fluxes over a Wheat Cropland in East China Using the Random Forest Model

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ATMOSPHERE 2022年第12期13卷 2080-2080页

作者： Zhang, Shiqi Duan, Zexia Zhou, Shaohui Gao, Zhiqiu Nanjing Univ Informat Sci & Technol Sch Atmospher Phys China Meteorol Adm Key Lab Aerosol Cloud PrecipitatClimate & Weather Nanjing 210044 Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Atmospher Boundary Layer Phys & Atmo Beijing 100029 Peoples R China

Modeling the heat and carbon dioxide (CO2) exchanges in agroecosystems is critical for better understanding water and carbon cycling, improving crop production, and even mitigating climate change, in agricultural regions. While previous studies mainly focused on simulations of the energy and CO2 fluxes in agroecosystems on the North China Plain, their corrections, simulations and driving forces in East China are less understood. In this study, the dynamic variations of heat and CO2 fluxes were simulated by a standalone version of the Simple Biosphere 2 (SiB2) model and subsequently corrected using a Random Forest (rf) machine learning model, based on measurements from 1 January to 31 May 2015-2017 in eastern China. Through validation with direct measurements, it was found that the SiB2 model overestimated the sensible heat flux (H) and latent heat flux (LE), but underestimated soil heat flux (G(0)) and CO2 flux (F-c). Thus, the rf model was used to correct the results modeled by SiB2. The rf model showed that disturbances in temperature, net radiation, the G(0) output of SiB2, and the F-c output of SiB2 were the key driving factors modulating the H, LE, G(0), and F-c. The rf model performed well and significantly reduced the biases for H, LE, G(0), and F-c simulated by SiB2, with higher R-2 values of 0.99, 0.87, 0.75, and 0.71, respectively. The SiB2 and rf models combine physical mechanisms and mathematical correction to enable simulations with both physical meaning and accuracy.

关键词： turbulent flux CO2 flux SiB2 model rf algorithm wheat field machine learning model

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A Semi-Automatic Method for Extracting Small Ground Fissures from Loess Areas Using Unmanned Aerial Vehicle Images

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REMOTE SENSING 2021年第9期13卷 1784-1784页

作者： Jia, Hongguo Wei, Bowen Liu, Guoxiang Zhang, Rui Yu, Bing Wu, Shuaiying Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Guangxi Nat Resources Survey & Monitoring Inst Nanning 530022 Peoples R China Southwest Petr Univ Sch Civil Engn & Geomat Chengdu 610500 Peoples R China

Remote sensing-based ground fissure extraction techniques (e.g., image classification, image segmentation, feature extraction) are widely used to monitor geological hazards and large-scale artificial engineering projects such as bridges, dams, highways, and tunnels. However, conventional technologies cannot be applied in loess areas due to their complex terrain, diverse textural information, and diffuse ground target boundaries, leading to the extraction of many false ground fissure targets. To rapidly and accurately acquire ground fissures in the loess areas, this study proposes a data processing scheme to detect loess ground fissure spatial distributions using unmanned aerial vehicle (UAV) images. Firstly, the matched filter (MF) algorithm and the first-order derivative of the Gaussian (FDOG) algorithm were used for image convolution. A new method was then developed to generate the response matrices of the convolution with normalization, instead of the sensitivity correction parameter, which can effectively extract initial ground fissure candidates. Directions, the number of MF/FDOG templates, and the efficiency of the algorithm are comprehensively considerate to conclude the suitable scheme of parameters. The random forest (rf) algorithm was employed for the step of the image classification to create mask files for removing non-ground-fissure features. In the next step, the hit-or-miss transform algorithm and filtering algorithm in mathematical morphology is used to connect discontinuous ground fissures and remove pixel sets with areas much smaller than those of the ground fissures, resulting in a final binary ground fissure image. The experimental results demonstrate that the proposed scheme can adequately address the inability of conventional methods to accurately extract ground fissures due to plentiful edge information and diverse textures, thereby obtaining precise results of small ground fissures from high-resolution images of loess areas.

关键词： ground fissure extraction loess landform modified MF-FDOG algorithm rf algorithm UAV image

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Study on habitat suitability and environmental variable thresholds of rare waterbirds

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 785卷 147316-147316页

作者： Wang, Cheng Liu, Hongyu Li, Yufeng Dong, Bin Qiu, Chunqi Yang, Jialing Zong, Ying Chen, Hao Zhao, Yongqiang Zhang, Yanan Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Sch Marine Sci & Engn Nanjing 210023 Peoples R China Anhui Agr Univ Sch Sci Hefei 230036 Peoples R China Jiangsu Yancheng Wetland Natl Nat Reserve Rare Birds Yancheng 224057 Jiangsu Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China

The conservation level of rare waterbirds reflects the quality of the regional ecological environment and wetlands, and suitable habitat patches and good environmental conditions are bases to support the activities of rare species in habitats. Establishing these conditions is also an important goal of habitat landscape and functional restoration. However, lack of these conditions limits population protection and habitat restoration of rare species. Based on the random forest (rf) algorithm and threshold indicator taxa analysis (TITAN), this paper performed habitat suitability assessment and environmental variable threshold analysis of rare waterbird species in Yancheng coastal wetlands. The results showed that the suitable area proportion of three waterbird species at different habitat sites was less than 20%. The unsuitable area proportions of red-crowned cranes and oriental storks at the CA habitat site were the highest, reaching 86.73% and 85.17%, respectively. In addition, analysis of the importance of environmental variables showed that the main influencing variables affecting the suitable habitat distribution of the three rare waterbirds were habitat type (T_hab), habitat area (A_hab), vegetation coverage (P_fvc), distance to farmland (D_far), distance to reeds (D_ree), ponds density (Ponds), distance to water surface (D_wat) and distance to main roads or seawalls (D_swa). These variables covered the type, area, coverage and distance indicators. With the exception of D_far, Ponds and D_swa, rare waterbirds had response thresholds to each environmental indicator, and these results supported the restoration of landscape structure and function of each habitat site. This study emphasized the importance of foods, water resources and hidden conditions for habitat selection in rare waterbirds. Finally, we proposed the maintenance and restoration patterns of the landscape structure and function of rare waterbird habitats, which are available for other coastal tidal we

关键词： Yancheng coastal wetlands Rare waterbirds Habitat suitability rf algorithm TITAN Habitat restoration

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Canopy-Height and Stand-Age Estimation in Northeast China at Sub-Compartment Level Using Multi-Resource Remote Sensing Data

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REMOTE SENSING 2023年第15期15卷 3738-3738页

作者： Guan, Xuebing Yang, Xiguang Yu, Ying Pan, Yan Dong, Hanyuan Yang, Tao Northeast Forestry Univ Sch Forestry Harbin 150040 Peoples R China Northeast Forestry Univ Key Lab Sustainable Forest Ecosyst Management Minist Educ Harbin 150040 Peoples R China Heilongjiang Acad Forestry Harbin 150040 Peoples R China

Stand age is a significant factor when investigating forest resource management. How to obtain age data at a sub-compartment level on a large regional scale conveniently and in real time has become an urgent scientific challenge in forestry research. In this study, we established two strategies for stand-age estimation at sub-compartment and pixel levels, specifically object-based and pixel-based approaches. First, the relationship between canopy height and stand age was established based on field measurement data, which was achieved at the Mao'er Mountain Experimental Forest Farm in 2020 and 2021. The stand age was estimated using the relationship between the canopy height, the stand age, and the canopy-height map, which was generated from multi-resource remote sensing data. The results showed that the validation accuracy of the object-based estimation results of the stand age and the canopy height was better than that of the pixel-based estimation results, with a root mean squared error (RMSE) increase of 40.17% and 33.47%, respectively. Then, the estimated stand age was divided into different age classes and compared with the forest inventory data (FID). As a comparison, the object-based estimation results had better consistency with the FID in the region of the broad-leaved forests and the coniferous forests. In addition, the pixel-based estimation results had better accuracy in the mixed forest regions. This study provided a reference for estimating stand age and met the requirements for stand-age data at the pixel and sub-compartment levels for studies involving different forestry applications.

关键词： object based pixel based canopy height GEDI rf algorithm

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Adaptive online terrain classification method for mobile robot based on vibration signals

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2021年第6期18卷

作者： Wang, Mingming Ye, Liming Sun, Xiaoyun Shijiazhuang Tiedao Univ Dept Elect & Elect Engn Shijiazhuang 050043 Hebei Peoples R China

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time-frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot's moving speed and achieve higher terrain classification accuracy.

关键词： Terrain classification mobile robot rf algorithm feature extraction

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