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Development of a machine learning algorithm for Efficient Localization of Damage in a Composite Structure Using Random Forest Technique

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第6期48卷 4793-4809页

作者： Shinagam, Rama Krishna Maruvada, Tarun Janjeti, Sathish Talari, Rahul Mani Datta Shinagam, Raghu Vamsi Gayatri Vidya Parishad College of Engineering Visakhapatnam 530 048 India

Composite materials offer a unique combination of properties that make them highly versatile and advantageous in numerous applications, particularly in aircraft and automobile industries. However, despite their advantages, composite materials are susceptible to crack propagation under fatigue stress due to the absence of endurance limit. This susceptibility poses a significant safety risk in aircraft and automobile applications. Therefore, developing a quick, simple, and reliable technique for early detection of cracks is crucial for enhancing the safety of these vehicles. machine learning models trained on vibration data offer a promising approach for real-time health monitoring and damage detection in composite structures. This study develops an efficient solution using a Random Forest regression model to predict crack location based on the first three natural frequencies obtained through impact hammer testing on various cracked composite structures. A dynamic signal analyzer, impact hammer, and acceleration sensor are employed to acquire the data of the first three natural frequencies, which are then used to train the Random Forest regression algorithm. Min-max scaling is applied to normalize the input data, further enhancing the efficiency of the proposed scheme. The performance of the Random Forest regression model is evaluated using R-squared score, mean absolute error (MAE) and mean squared error (MSE). After applying the min-max scaler, the performance of the model is evaluated with the MAE, MSE, and R-squared score, which are 7.164, 137.309, and 0.972, respectively. The high R-squared score of the fitted Random Forest algorithm demonstrates its strong ability to accurately predict the crack location. The proposed method is further validated through experiments conducted on a cracked composite structure. The error between the measured and predicted values using the present scheme is found to be only 9.6%, thus validating the effectiveness of the proposed app

关键词： Composite structure Damage localization Data normalization High efficiency Impact hammer test machine learning algorithm Random forest regression model

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machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD

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JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES 2021年第7期28卷 593-603页

作者： Feng, Gong Zheng, Kenneth I. Li, Yang-Yang Rios, Rafael S. Zhu, Pei-Wu Pan, Xiao-Yan Li, Gang Ma, Hong-Lei Tang, Liang-Jie Byrne, Christopher D. Targher, Giovanni He, Na Mi, Man Chen, Yong-Ping Zheng, Ming-Hua Xian Med Univ Xian Peoples R China Wenzhou Med Univ Dept Hepatol NAFLD Res Ctr Affiliated Hosp 1 2 Fuxue Lane Wenzhou 325000 Peoples R China Wenzhou Med Univ Dept Pathol Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Dept Lab Med Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Dept Endocrinol Affiliated Hosp 1 Wenzhou Peoples R China Univ Hosp Southampton Southampton Natl Inst Hlth Res Biomed Res Ctr Southampton Gen Hosp Southampton Hants England Univ Verona Sect Endocrinol Diabet & Metab Dept Med Verona Italy Azienda Osped Univ Integrata Verona Verona Italy Xian Med Univ Dept Gastroenterol Affiliated Hosp 1 Xian Peoples R China Wenzhou Med Univ Inst Hepatol Wenzhou Peoples R China Key Lab Diag & Treatment Dev Chron Liver Dis Zhej Wenzhou Peoples R China

Background The presence of significant liver fibrosis is a key determinant of long-term prognosis in non-alcoholic fatty liver disease (NAFLD). We aimed to develop a novel machine learning algorithm (MLA) to predict fibrosis severity in NAFLD and compared it with the most widely used non-invasive fibrosis biomarkers. Methods We used a cohort of 553 adults with biopsy-proven NAFLD, who were randomly divided into a training cohort (n = 278) for the development of both logistic regression model (LRM) and MLA, and a validation cohort (n = 275). Significant fibrosis was defined as fibrosis stage F >= 2. MLA and LRM were derived from variables that were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. Results In the training cohort, the variables selected by LASSO algorithm were body mass index, pro-collagen type III, collagen type IV, aspartate aminotransferase and albumin-to-globulin ratio. The diagnostic accuracy of MLA showed the highest values of area under the receiver operator characteristic curve (AUROC: 0.902, 95% CI 0.869-0.904) for identifying fibrosis F >= 2. The LRM AUROC was 0.764, 95% CI 0.710-0.816 and significantly better than the AST-to-Platelet ratio (AUROC 0.684, 95% CI 0.605-0.762), FIB-4 score (AUROC 0.594, 95% CI 0.503-0.685) and NAFLD Fibrosis Score (AUROC 0.557, 95% CI 0.470-0.644). In the validation cohort, MLA also showed the highest AUROC (0.893, 95% CI 0.864-0.901). The diagnostic accuracy of MLA outperformed that of LRM in all subgroups considered. Conclusions Our newly developed MLA algorithm has excellent diagnostic performance for predicting fibrosis F >= 2 in patients with biopsy-confirmed NAFLD.

关键词： diagnosis fibrosis liver biopsy machine learning algorithm NAFLD

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machine learning algorithm based on jaw feature points assist complex maxillary and mandibular reconstruction

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JOURNAL OF STOMATOLOGY ORAL AND MAXILLOFACIAL SURGERY 2023年第1期124卷 101343页

作者： Han, Jing Liu, Zijia Zhou, Zijie Liu, Yige Zhai, Guangtao Liu, Jiannan Shanghai Jiao Tong Univ Shanghai Peoples Hosp 9 Sch Med Dept Oral & Maxillofacial Head & Neck Oncol Shanghai 200011 Peoples R China Shanghai Jiao Tong Univ Coll Stomatol Shanghai 200011 Peoples R China Natl Clin Res Ctr Oral Dis Shanghai 200011 Peoples R China Shanghai Key Lab Stomatol Shanghai 200011 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200241 Peoples R China

Purposes: Large-scale jaw reconstruction can hardly achieve satisfactory results only by relying on doctors' experience. In this study, we assessed a new approach using a machine learning algorithm based on jaw feature points to assist complex jaw reconstruction in patients with maxillary and mandibular defects. Methods: One hundred and two computed tomography (CT) data on the jaw were collected and 16 skeletal marker points on the jaw were selected. The machine learning algorithm learned the positional relationship between points and built a model, which was used to predict the coordinate position of an unknown point. Then the model was used for a surgical plan in clinical cases. Results: The linear regression model based on machine learning can control the error within 3 mm. In linear models, Lasso has a slight advantage over the others. We used Lasso to predict the missing points for two patients with maxillary and mandibular defect, respectively. The operation was carried out as planned, and the defects were successfully repaired. Conclusions: The restoration of jaw feature points based on a machine learning algorithm is expected to solve large-scale jaw defects without contralateral reference. (c) 2022 Published by Elsevier Masson SAS.

关键词： machine learning algorithm Jaw reconstruction Jaw feature points

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Classification of Foot Pressure Images Using machine learning algorithm

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Computer Systems Science & Engineering 2022年第4期41卷 187-196页

作者： P.Ramya B.Padmapriya S.Poornachandra Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatore641107Tamil NaduIndia Department of Biomedical Engineering PSG College of TechnologyCoimbatore641004Tamil NaduIndia Department of Electronics and Communication Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062Tamil NaduIndia

Arthritis is an acute systemic disease of a joint accompanied by *** developed countries,it mainly causes disability among people over 50 years of *** Arthritis is a type of arthritis that occurs commonly among *** incidence of arthritis is higher in females than in *** is no permanent diagnosis method for arthritis,but if it was identiﬁed in the early stages based on the foot pressure,it can be diagnosed before attaining the critical stage of Rheumatoid *** analysis and study of arthritis patients were done using design thinking *** thinking is a problem-solving methodology that is used toﬁnd a solution for the identiﬁcation of the early stage of *** process consists ofﬁve stages follows Empathy,Deﬁne,Ideate,Prototype,and *** deﬁne the problem statement,the Empathy was done with the arthritis patients to know the difﬁculties faced by *** paper proposes a measurement technique of early measurement of arthritis using a non-invasive *** helps us to detect arthritis using a foot pressure pad that was designed with piezoresistive material and the feature classiﬁcation was done using Weka.

关键词： Piezoresistive material velostat carbon loaded piezo resistiveﬁlm machine learning algorithm SVM MLP classiﬁcation design thinking

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GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

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Natural Hazards Research 2023年第3期3卷 420-436页

作者： Saikh, Nur Islam Mondal, Prolay Department of Geography Raiganj University West Bengal Raiganj 733134 India

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic (ROC) Curve. Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in t

关键词： Flood susceptibility GIS machine learning algorithm Pagla river Receiver Operating Characteristic

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Detection of COVID-19 Infection from Clinical Findings Using machine learning algorithm 1st

Detection of COVID-19 Infection from Clinical Findings Using...

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1st international conference Ambient Intelligence in Health Care (ICAIHC)

作者： Durgadevi, Velusamy Arunagiri, Bharath Dhanapal, Vignesh Seeniraj, Yogesh Krishnan Thirugnanam, Shashangan M Kumarasamy Coll Engn Dept Comp Sci & Engn Karur 639113 India

ISBN: (纸本)9789811960703;9789811960680;9789811960673

COVID-19 infection is a transmissible virus causing acute respiratory syndrome spreading worldwide. The number of patients infected by this deadly virus increases steadily, causing a high mortality rate. Hence, it is crucial to diagnose and identify the COVID-19 infection for earlier treatment of the patients. This study has applied four algorithms, namely, Logistic Regression (LR), Nu-Support Vector machine (Nu-SVM), Multi-layer perceptron (MLP) and Naive Bayes (NB) to identify COVID-19 infection. The clinical laboratory findings of 600 individuals were taken from Hospital Isrelita Albert Einstein, Sao Paulo, Brazil, used in this study. We have selected significant features using Random forest-based recursive feature elimination for predicting the infection. Experiments are conducted with 90% training and 10% testing data. The performance result shows that the Nu-SVM algorithm obtained the prediction accuracy of 95% with 100% sensitivity and 94.23% specificity in predicting the infection. To our knowledge, the result achieved by Nu-SVM is the highest in the literature. Hence, the model can be used as a tool for the initial prediction of COVID-19 disease.

关键词： Clinical findings COVID-19 infection machine learning algorithm Nu-support vector machine

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The application of improving machine learning algorithm and voice technology in the teaching evaluation of ideological and political education

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第4期22卷 1277-1285页

作者： Sun, Qimeng Jiangxi Univ Tradit Chinese Med Sci & Technol Coll Nanchang 330004 Jiangxi Peoples R China

After entering the new millennium, the computing capacity of information terminal has shown a rapid development. This progress has caused cross generational changes in various fields, especially in the field of communication technology, which directly spawns a new field. Compared with the development speed of information terminal, the development of communication technology is always in the position of "catch-up", and the main work performer is the traditional data form. This backward leads to the primitive evaluation of Ideological and political education. This paper mainly studies the application of improved machine learning algorithm and voice technology in the teaching evaluation of Ideological and political education. The weighted naive Bayesian algorithm is applied to the teaching evaluation of Ideological and political education creatively. By inference of hypothesis model, the intervention curve of various conditions on the evaluation results is verified. The influence of class attribute probability on condition assignment is obtained, and it is used as a calculation tool for our evaluation of Ideological and political education teaching. The experimental results show that the improved weighted naive Bayesian algorithm can better integrate the speech technology and improve the evaluation accuracy.

关键词： Teaching evaluation machine learning algorithm data mining weighted naive Bayesian

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Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm

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PRECISION AGRICULTURE 2022年第5期23卷 1688-1706页

作者： Xie, Ziwen Chen, Song Gao, Guizhen Li, Hao Wu, Xiaoming Meng, Lei Ma, Yuntao China Agr Univ Coll Land Sci & Technol Beijing 100193 Peoples R China Jiangsu Acad Agr Sci Inst Ind Crops Nanjing 210014 Peoples R China Chinese Acad Agr Sci Key Lab Biol & Genet Improvement Oil Crops Minist Agr Oil Crops Res Inst Wuhan 430062 Peoples R China Western Michigan Univ Dept Geog Environm & Tourism Kalamazoo MI 49008 USA

Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R-2 = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.

关键词： Brassica napus L. Flowering date machine learning algorithm Clustering analysis Variety classification

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Proximity Estimation with BLE RSSI and UWB Range Using machine learning algorithm 13

Proximity Estimation with BLE RSSI and UWB Range Using Machi...

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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Debnath, Satinath O'Keefe, Kyle Department of Geomatics Engineering CalgaryAB Canada

ISBN: (纸本)9798350320114

Measuring the distance between two indoor mobile phones has recently become important in the context of pandemic contact tracing. Mobile phone location has improved as new technologies are added to smartphones. Ultra-wideband (UWB) ranging radios capable of centimetre-level ranging accuracies have been included in some flagship smartphone models beginning in 2019 but remain uncommon. The ranging radios compute distances using two-way time of flight approaches, providing up to centimetre-level accuracy in line-of-sight (LOS) conditions. Other mobile phones in the market today use Bluetooth Low Energy (BLE) to estimate proximity between two Bluetooth-enabled devices using the Received Signal Strength Indicator (RSSI). However, this technology suffers from drawbacks since the varying propagation environment inhibits stable RSSI. This paper presents a method where a subset of users are carrying high-end UWB-enabled smartphones. These users collect precise UWB ranges in addition to BLE RSSI. A classical machine learning (ML) algorithm is then trained with UWB ranges as truth values to estimate distances based on RSSI. A random forest ML technique is described for RSSI to distance modeling. The majority of the users, with only BLE, then estimate their relative distances using the trained model. The model was trained using UWB and BLE observations collected in an empty room environment. The model was then tested using RSSI measurements from the same empty and a second room containing typical office furniture. When testing on the training environment, 213 test samples were used. In the second office environment 200 RSSI samples were used to estimate distance in various locations. © 2023 IEEE.

关键词： BLE RSSI distance estimation machine learning algorithm UWB range

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Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit

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INTERNATIONAL WOUND JOURNAL 2022年第7期19卷 1637-1649页

作者： Xu, Jie Chen, Danxiang Deng, Xiaofang Pan, Xiaoyun Chen, Yu Zhuang, Xiaoming Sun, Caixia Wenzhou Med Univ Dept Thorac Surg Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Dept Breast Surg Affiliated Hosp 1 Wenzhou Peoples R China Wenzhou Med Univ Nursing Dept Wenzhou Peoples R China Wenzhou Med Univ Nursing Dept Affiliated Hosp 1 Wenzhou 325000 Zhejiang Peoples R China

The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large-scale clinical data. A retrospective cohort study design was employed to develop and validate a top-performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five-fold cross-validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23-point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 +/- 0.07 and 0.84 +/- 0.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.

关键词： electronic medical record machine learning algorithm pressure injury risk prediction

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