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TBM penetration rate prediction ensemble model based on full-scale linear cutting test

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年 131卷

作者： Wang, Yaxu Wang, Jiwen Wang, Ruirui Liu, Bin Li, Yao Shandong Univ Geotech & Struct Engn Res Ctr Jinan 250061 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China Shandong Jianzhu Univ Sch Civil Engn Jinan 250101 Peoples R China Shandong Univ Sch Civil Engn Jinan 250061 Peoples R China Shandong Univ Data Sci Inst Jinan 250061 Peoples R China Shandong Univ Geotech & Struct Engn Res Ctr 17923 Jingshi Rd Jinan 250061 Peoples R China

Accurate and effective prediction of TBM penetration rate (PR) is significant for ensuring TBM tunneling safety, improving tunneling efficiency and optimizing construction cost. Establishing a prediction model is usually an effective method to solve this critical problem. The type of TBM penetration rate prediction models can be divided into two categories: theoretical models and empirical models. Each model has its advantages, but there are also shortcomings. To make the advantages of the two models complement each other and further improve the accuracy and application scope of the prediction model, this paper proposes a prediction ensemble model of PR based on a full-scale linear cutting test and machine learning algorithm. A semi-theoretical TBM penetration rate prediction model is established through the full-scale linear cutting test and the rock-breaking analysis of granite and sandstone with different strengths. Based on the XGBoost intelligent algorithm, an empirical model of the penetration rate prediction is also established. On this basis, the Bagging algorithm combines these two models to form an ensemble prediction model of TBM penetration rate. We collected rock mass and operational parameters in the Hangzhou Second Water Source Water Transfer Channel Project to verify the semi-theoretical, empirical, and ensemble models, respectively. The results show that the three models' prediction errors are all less than 15%, and the ensemble model has a smaller error of 10.2%. This result confirms that the ensemble model can effectively improve prediction accuracy and has good engineering applicability.

关键词： TBM performance prediction Linear cutting test machine learning algorithm Engineering verification

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DESIGN AND IMPLEMENTATION OF A PORTABLE AUTOMATED AUDIOMETER FOR HEARING CLASSIFICATION USING machine learning APPROACHES

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2022年第5期34卷

作者： Raja Sankari, V. M. Snekhalatha, U. Rajalakshmi, T. SRM Inst Sci & Technol Coll Engn & Technol Dept Biomed Engn Kattankulathur 603203 Tamil Nadu India SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Commun Engn Kattankulathur 603203 Tamil Nadu India

Audiometric tests can identify the hearing loss at specific frequencies using the audiogram. The aim and objectives of the study were (i) to develop an automated audiometer for self-diagnosing the hearing ability of the patient;(ii) to extract the features from the acoustic signals and to classify the normal and profound hearing loss patients using different machine learning algorithms;(iii) to validate the hearing loss classification using six-frequency average (6-FA) method based on simple linear regression analysis and machine learning algorithms. The study is conducted among 150 patients, including 75 patients with normal hearing ability and 75 patients with profound hearing loss. The total population of 150 underwent audiometric test both in the soundproof audiometric room and in the normal field environment. Based on the patient response, the intensity and frequency are changed automatically, and the audiogram is plotted by the principle of Artificial Neural Network learning procedures. The overall accuracy produced by classification of normal and profound hearing loss patients using Support Vector machine (SVM), k-Nearest Neighbor classifier, and Naive Bayes classifier is 97%, 96%, and 95%, respectively. The results indicated that the SVM classifier outperforms the other two classifiers well. The preliminary audiometric test can be performed remotely and then consulted with an audiologist. Thus, the patient could operate the developed prototype independently and get a consultation from trained medical personnel.

关键词： Audiogram Audiometry machine learning algorithm Profound hearing loss

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Application of machine learning techniques in the prediction of excess lifetime cancer risks of agricultural byproducts used as building and construction materials

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CLEANER WASTE SYSTEMS 2023年 5卷

作者： Oyebisi, Solomon Owamah, Hilary Covenant Univ Dept Civil Engn PMB 1023 Km 10 Idiroko Rd Ota Nigeria Delta State Univ Dept Civil & Environm Engn Abraka Nigeria

Recycling improves the circular economy and resource sustainability by using agricultural waste to create new products. However, agricultural byproducts resulting from the recycling of agricultural waste materials contain naturally occurring radionuclides with potential risks to human health and the environment. Therefore, this article provides an overview of relevant literature on radiological properties of agricultural byproducts (rice husk ash, mussel shell, palm oil clinker, and palm oil fuel ash), prioritizing their specific activities (226Ra, 232Th and 40K). Consequently, absorbed gamma dose rates (AGDR), annual effective dose rates (AEDR) and excess lifetime cancer risks (ELCR) of the agricultural byproducts studied were determined. The specific concentrations, AGDR, AEDR, and ECLR were trained, validated, and tested using various machine learning algorithms. An evaluation of the radiological properties of all agricultural byproducts examined revealed that they pose no risk of cancer. Additionally, compared to support vector machine, regression trees, ensemble trees, Gaussian process regression, and neural networks, linear regression yielded the best performance metrics, making it the most suitable technique for predicting excess lifetime cancer risks of the surveyed agricultural byproducts.

关键词： Cancer machine learning algorithm Radioactivity Recycling Sustainable production Waste management

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Comparing algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis

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FORESTS 2023年第9期14卷

作者： Huang, Tianbao Ou, Guanglong Xu, Hui Zhang, Xiaoli Wu, Yong Liu, Zihao Zou, Fuyan Zhang, Chen Xu, Can Kunming Gen Survey Nat Resources China Geol Survey Kunming 650111 Peoples R China Minist Nat Resources Technol Innovat Ctr Nat Ecosyst Carbon Sink Kunming 650111 Peoples R China Southwest Forestry Univ Key Lab Southwest Mt Forest Resources Conservat & Minist Educ Kunming 650233 Peoples R China

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R-2 value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation.

关键词： forest aboveground biomass machine learning algorithm remote sensing Pinus yunnanensis forest stacking ensemble Yunnan Province China

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Supervised machine learning Classification algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network

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OPTICAL MEMORY AND NEURAL NETWORKS 2020年第3期29卷 244-256页

作者： Abhale, Ashwini B. Manivannan, S. S. DY Patil Coll Engn & Technol Akurdi India Vellore Inst Technol Vellore Tamil Nadu India

From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet and it is being used for providing facilities to human beings. Wireless sensor have been used for various application such as fire safety, military application, petroleum industry, security system, monitoring and environmental condition and many more. WSN node exposes itself to various security related attacks due to low battery power supply, low bandwidth support, data transmission over multi hop node, dependency on intermediate or other nodes, distributed in nature and self-organization. The WSN attacks observe in all layers of OSI model. Wireless sensor nodes has various issues because of that, it experiences number problem related to its functionalities and some malfunction due to attacks. It is require to build defence and network monitoring system for identifying attacks and prevent them. Intrusion detection system (IDS) plays an important role to detect threads inside the system and generate the alert related to the attack. In this work, supervised classification models for intrusion detection are built using such as Random Forest classifier, Support Vector machine, Decision Tree Classifier, LGBM Classifier, Extra Tree Classifier, Gradient Boosting Classifier, Ada Boost Classifier, K Nearest Neighbour Classifier, MLP Classifier, Gaussian Naive Bayes Classifier and Logistic Regression Classifier. The NSLKDD, i.e. Modified version of the KDD99 Data Set on which we checks these algorithms. Experimental results how the highest accuracy relative to other classification systems in the support vector machine.

关键词： machine learning algorithm classification wireless sensor network intrusion detection system accuracy performance matrix

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Diagnosis of Breast Cancer using machine learning algorithms-A Review

Diagnosis of Breast Cancer using Machine Learning Algorithms...

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Emerging Trends in Communication, Control and Computing (ICONC3), International Conference on

作者： Aditi Kajala V K Jain Computer Science and Engineering School of Engineering and Technology Mody University of Science and Technology Lakshmangarh India

ISBN: (数字)9781728114200

ISBN: (纸本)9781728114217

Breast cancer is one of the common diseases specifically in women now days. It has become the second main reason of cancer death in females. Every year 4.5-5% new cancer cases are recorded and increasing the morbidity at worldwide. It has proved that early detection of any cancer when followed up with appropriate diagnosis and treatment can increase the survival rate of the patients. Breast cancer is diagnosed by mammography. Mammograms are films generated by radiologist with a device. These mammograms are observed and diagnosed by the oncologist for further treatment. Since all general hospitals do not have the specialist and patients used to wait for their report. So waiting for diagnosing a breast cancer may take time. This delay may be responsible for cancer spreading and reducing the survival rate of the patient. Therefore machine learning can be used to diagnose breast cancer by a computer to make the diagnosing efficient and effective. This does not mean to replace expert or physician by computer but it means that computer can assist the expert for better understanding the particular case and the results can be produced early. This paper presents a brief summary on breast cancer diagnosis using machine learning algorithms used to increase the efficiency and effectiveness of predicting cancer. The correct diagnosis and accurate classification are the main objective of the reviewed papers.

关键词： Mammograms machine learning algorithm Wisconsin Breast Cancer Dataset (WBCD) Breast Cancer machine learning algorithms Digital Mammography Survival Rate Computers Diagnosis Professional personnel Artificial Intelligence machine learning breast neoplasm diagnosis General Hospitals Oncologists

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"Geology-geophysics-data mining" integration to enhance the identification of deep fault-controlled paleokarst reservoirs in the Tarim Basin

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MARINE AND PETROLEUM GEOLOGY 2023年 158卷

作者： Tian, Fei Zhang, Jiangyun Zheng, Wenhao Zhou, Hui Ma, Qihao Shen, Chunguang Ma, Qingyou Lan, Mingjie Liu, Yunchen Chinese Acad Sci Inst Geol & Geophys CAS Engn Lab Deep Resources Equipment & Technol Beijing 100029 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing 100049 Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China Bohai Drilling Corp Sinopec Shengli Petr Engn Co Ltd Dongying 257200 Peoples R China PetroChina Tarim Oilfield Co Korla 841000 Xinjiang Peoples R China SINOPEC Northwest Oilfield Co Urumqi 830011 Xinjiang Peoples R China

Large-scale strike-slip faults play a dual role as conduits for fluid flow, channeling both atmospheric freshwater and deep hydrothermal fluids, thereby creating porous carbonate reservoirs at significant depths. Moreover, these faults serve as vertical conduits, connecting deep source rocks to oil and gas reservoirs capable of generating substantial hydrocarbon fields. In recent years, the Tarim Basin has yielded numerous high-yield reservoirs along large-scale strike-slip faults, exhibiting exceptional hydrocarbon potential. Nevertheless, the intricate heterogeneity of deep-seated traps engendered by these faults presents a formidable challenge in accurately determining reservoir geometry, consequently complicating the drilling of high-yield wells. Currently, enhancing the precision of reservoir identification stands as a paramount issue within the Tarim *** paper introduces an innovative solution termed the "Geology-Geophysics-Data Mining" workflow, designed to enhance the resolution of multiscale geophysics data through the lens of geological insights. Drawing inspiration from the geological model of "fault-controlled paleokarst," and utilizing a fusion of well logs and seismic data, we elect seismic impedance as a discerning parameter to differentiate between porous reservoirs and impermeable host rocks. The proposed methodology unfolds through several ***, noise reduction and frequency domain extension of the original seismic data are achieved through spectral shaping and diffusion filtering algorithms. Subsequently, the impedance curve of the logging wave is computed, establishing a non-linear correlation with seismic waveforms using a waveform indication inversion algorithm, ultimately yielding inverse impedance data. In the subsequent phase, the impedance originating from stable sedimentary features is quantified and subsequently extracted from the inversely derived seismic data. This step accentuates the high-quality reservoirs formed vi

关键词： Fault-controlled paleokarst reservoirs machine learning algorithm Reservoir identification Tarim basin

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Yield and plant height predictions of irrigated maize through unmanned aerial vehicle in North Florida

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 215卷

作者： de Sa Leitao, Diego Arruda Huggins Sharma, Ayush K. Singh, Aditya Sharma, Lakesh K. Univ Florida Soil Water & Ecosyst Sci Dept Gainesville FL 32611 USA Univ Florida Agr & Biol Engn Dept Gainesville FL 32611 USA

Monitoring crops and producing correct yield estimates are both crucial components of decision-making in agricultural systems. This is particularly important for crops like maize, with a national gross production value above US $82 billion in 2021. Several crop parameters, e.g., plant height (PH), leaf nutrient status, etc., are in use for improving maize grain yield (MGY) prediction models. Traditional PH measurements can be time-consuming and labor-intensive;thus, unmanned aerial vehicles (UAVs) might be efficient and practical tools to predict in-season MGY. Two small-plot trials were conducted in North Florida to estimate in-field PH (IFPH) using UAVs (UAV-PH) and compare different regression approaches to estimate MGY using different time points (from 17 to 133 days after planting [DAP]). A multispectral camera was mounted onto an Aurelia X6 Standard hexacopter to capture crop canopy height. Overall, the IFPH values were higher than UAV-PH but strongly correlated (r = 0.95). Moderate to strong correlations were observed between MGY and PH at all time points except 17 DAP. The prediction of MGY using IFPH or UAV-PH was better through machine learning algorithms (Random forest re-gressor [RFR] and Support vector regression [SVR]) compared to simple/multiple linear regression (S/MLR), with R-2, MAE, RMSE, and nRMSE ranges of 0.91-0.94, 553.86-1574.62 kg ha(-1), 708.94-195.04 kg ha(-1), and 14.2-39.1 % for best-performing models of RFR and SVR, respectively. Furthermore, S/MLR usually over-estimated low-yielding plots (MGY < 12000 kg ha(-1)) and underestimated high-yielding plots (MGY > 12000 kg ha(-1)). The results showed that UAVs are a potential tool for quicker decision-making in maize cropping systems in North Florida since the crop-height model accurately estimated ground PH measurements (IFPH) at different time points.

关键词： Crop-height model machine learning algorithm Remote sensing Structure from motion Zea mays

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Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

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MOVEMENT DISORDERS 2021年第2期36卷 460-470页

作者： Chougar, Lydia Faouzi, Johann Pyatigorskaya, Nadya Yahia-Cherif, Lydia Gaurav, Rahul Biondetti, Emma Villotte, Marie Valabregue, Romain Corvol, Jean-Christophe Brice, Alexis Mariani, Louise-Laure Cormier, Florence Vidailhet, Marie Dupont, Gwendoline Piot, Ines Grabli, David Payan, Christine Colliot, Olivier Degos, Bertrand Lehericy, Stephane Sorbonne Univ CNRS Paris Brain Inst ICM INSERMUMR 7225UMR S 1127UMR 7225U1127 Paris France ICM Movement Invest & Therapeut Team MOVIT Paris France Ctr NeuroImagerie Rech CENIR ICM Paris France Pitie Salpetriere Univ AP HP Dept Neuroradiol Paris France INRIA Paris France Univ Paris 07 Fac Med Paris France Ctr Invest Clin Neurosci ICM Paris France Team Neurogenet Fondamentale & Translat ICM Paris France Hop La Pitie Salpetriere AP HP Dept Malad Syst Nerveux Clin Mouvements Anormaux Paris France Univ Bourgogne Dijon France CHU Francois Mitterrand Dept Neurol Dijon France Hop Univ Nimes BESPIM Nimes France Hop La Pitie Salpetriere AP HP Serv Pharmacol Clin Paris France Coll France Dynam & Pathophysiol Neuronal Networks Team CNRSMemoLife Labex INSERMCtr Interdisciplinary Res BiolUMR7241U10 Paris France Sorbonne Paris Nord Univ Avicenne Univ Hosp Dept Neurol Bobigny France

Background machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA-P), and 23 with MSA of the cerebellar variant (MSA-C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner-dependent effects, we tested two types of normalizations using patient data or healthy control data. Results In the replication cohort, high accuracies were achieved using volumetry in the classification of PD-PSP, PD-MSA-C, PSP-MSA-C, and PD-atypical parkinsonism (balanced accuracies: 0.840-0.983, area under the receiver operating characteristic curves: 0.907-0.995). Performances were lower for the classification of PD-MSA-P, MSA-C-MSA-P (balanced accuracies: 0.765-0.784, area under the receiver operating characteristic curve: 0.839-0.871) and PD-PSP-MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. Conclusions A machine learning approach based on volumetry enabled accurate classification of subjects with early-stage

关键词： Parkinson&apos s disease progressive supranuclear palsy multiple system atrophy multimodal magnetic resonance imaging machine learning algorithm

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Droplet Encoding-Pairing Enabled Multiplexed Digital Loop-Mediated Isothermal Amplification for Simultaneous Quantitative Detection of Multiple Pathogens

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ADVANCED SCIENCE 2023年第7期10卷 e2205863页

作者： Cai, Dongyang Wang, Yu Zou, Jingjing Li, Zhujun Huang, Enqi Ouyang, Xiuyun Que, Zhiquan Luo, Yanzhang Chen, Zhenhua Jiang, Yanqing Zhang, Guohao Wu, Hongkai Liu, Dayu South China Univ Technol Affiliated Hosp 2 Sch Med Dept Lab Med Guangzhou 510180 Peoples R China South China Univ Technol Coll Food Sci & Engn Guangzhou 510640 Peoples R China Beijing Baicare Biotechnol Co Ltd Beijing 102206 Peoples R China Hong Kong Univ Sci & Technol Dept Chem Hong Kong Peoples R China Guangdong Engn Technol Res Ctr Microfluid Chip Med Guangzhou 510180 Peoples R China Clin Mol Med & Mol Diag Key Lab Guangdong Prov Guangzhou 510180 Peoples R China

Despite the advantages of digital nucleic acid analysis (DNAA) in terms of sensitivity, precision, and resolution, current DNAA methods commonly suffer a limitation in multiplexing capacity. To address this issue, a droplet encoding-pairing enabled DNAA multiplexing strategy is developed, wherein unique tricolor combinations are deployed to index individual primer droplets. The template droplets and primer droplets are sequentially introduced into a microfluidic chip with a calabash-shaped microwell array and are pairwise trapped and merged in the microwells. Pre-merging and post-amplification image analysis with a machine learning algorithm is used to identify, enumerate, and address the droplets. By incorporating the amplification signals with droplet encoding information, simultaneous quantitative detection of multiple targets is achieved. This strategy allows for the establishment of flexible multiplexed DNAA by simply adjusting the primer droplet library. Its flexibility is demonstrated by establishing two multiplexed (8-plex) droplet digital loop-mediated isothermal amplification (mddLAMP) assays for individually detecting lower respiratory tract infection and urinary tract infection causative pathogens. Clinical sample analysis shows that the microbial detection outcomes of the mddLAMP assays are consistent with those of the conventional assay. This DNAA multiplexing strategy can achieve flexible high-order multiplexing on demand, making it a desirable tool for high-content pathogen detection.

关键词： droplet encoding-pairing droplet microfluidics machine learning algorithm multiplexed droplet digital nucleic acid analysis quantitative detection of pathogens

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