检索结果-内蒙古大学图书馆

Wearable sensors for classification of load-handling tasks with machine learning algorithms in occupational safety and health: a systematic literature review

引用

ERGONOMICS 2025年 1-21页

作者： Peters, M. Potthast, W. Wischniewski, S. Komnik, I. Fed Inst Occupat Safety & Hlth Dortmund Germany Univ Cologne German Sports Cologne Germany

Ergonomic assessments are critical to preventing work-related musculoskeletal disorders. The integration of machine learning with wearable sensor technology offers new approaches to risk assessment by capturing external forces and non-ergonomic working conditions. We conducted a systematic literature search, reviewing 851 studies from PubMed, Web of Science and Embase, and included 15 studies in our analysis. This review summarises and critically discusses these studies, which focus on posture classification, activity duration and external weight estimation in load handling tasks. Although the results are promising, current research covers only a few aspects, with limited emphasis on the measurement of external forces. Furthermore, many studies faced fundamental issues such as small sample sizes and limited access to research data and algorithms. Future advances in this area could greatly benefit from the sharing of datasets and algorithms, thereby increasing the comparability and robustness of findings.

关键词： Wearable sensors risk assessment machine-learning algorithms working activities load detection work-related musculoskeletal disorders

来源：评论

学校读者我要写书评

暂无评论

machine-learning algorithms for Ischemic Heart Disease Prediction: A Systematic Review

引用

CURRENT CARDIOLOGY REVIEWS 2023年第1期19卷 87-99页

作者： Hani, Salam H. Bani Ahmad, Muayyad M. Univ Jordan Fac Nursing Amman Jordan Al Al Bayt Univ Fac Nursing Mafraq Jordan

Purpose This review aims to summarize and evaluate the most accurate machine-learning algorithm used to predict ischemic heart disease. Methods This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore. Results Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning. Conclusion Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations. This review is registered at PROSPERO;the registration number is: CRD42021288599.

关键词： Big data data mining machine-learning algorithms prediction ischemic heart disease PRISMA

来源：评论

学校读者我要写书评

暂无评论

machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia

引用

MODELING EARTH SYSTEMS AND ENVIRONMENT 2022年第3期8卷 3719-3736页

作者： Woldemariam, Gezahegn Weldu Tibebe, Degefie Mengesha, Tesfamariam Engida Gelete, Tadele Bedo Ethiopian Space Sci & Technol Inst ESSTI Entoto Observ & Res Ctr EORC Dept Remote Sensing & Applicat Res & Dev POB 33679 Addis Ababa Ethiopia Addis Ababa Univ POB 1176 Addis Ababa Ethiopia Haramaya Univ Sch Geog & Environm Studies Geoinformat Sci Program POB 138 Dire Dawa 3220 Ethiopia

Natural and human-induced drivers within the constraints of multiple socioeconomic and political conditions have intensified the extent of land use and land cover (LULC) change at different scales. Hence, up-to-date spatial and statistical information about LULC change is of utmost importance for the development and implementation of appropriate Earth resource management strategies and monitoring environmental changes. With the increased availability of remotely sensed data from various Earth observation satellite and continuous advances in computing technology, these pieces of information are commonly retrieved based on automated digital image classification algorithms. However, in-depth quantitative evaluation of different classification algorithms is critical to select an efficient classifier or multiple ways to obtain more accurate land use maps. Taking selected hydrological catchments of the Lake Haramaya Watershed in the East Hararghe Ethiopian highland as an example, we statistically compared the land use classification performance of three nonlinear machine-learning algorithms (MLAs), namely: support vector machine (SVM), random forest (RF), and artificial neural network (ANN) and object-based image analysis (OBIA) using open-source Landsat-8 satellite images. At the same time, the classification accuracy was assessed using sample points from high spatial resolution Google-Earth Pro image. The confusion matrix analysis has indicated that the generated land use maps had a divergent classification accuracy. However, the SVM achieved the highest overall classification accuracy (94%) compared to the RF (92%) and the ANN (89%). On the contrary, the OBIA classifier achieved the least overall accuracy (75%) compared to the former. Thus, the results presented here reveal that though all supervised MLAs can be considered robust classifiers, the SVM algorithm was found to be the best classifier to improve the classification accuracy at an individual land use class lev

关键词： Artificial neural network Earth observation satellite Object-based image analysis LULC change machine-learning algorithms Random forest Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Prediction of impact behavior of notched composite pipes repaired with different composite patches by machine learning algorithms

引用

JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY 2025年第10期39卷 1521-1558页

作者： Bozkurt, Ilyas Pala, Zeydin Mus Alparslan Univ Architecture & Engn Fac Dept Mech Engn Mus Turkiye Mus Alparslan Univ Architecture & Engn Fac Dept Software Engn Mus Turkiye

In this study, the impact behavior of notched glass fiber reinforced composite pipes repaired by patch bonding is investigated and the impact performance is predicted by different machine learning algorithms. The effects of patch material, patch thickness, adhesive type and impact velocity on peak force (PF), absorbed energy (AE), maximum displacement (MD) and failure zones were investigated. In the finite element model, Progressive failure analysis based on the combination of Hashin failure criteria and Cohesive zone model (CZM) with Bilinear traction-separation law using MAT-54 material model with LS DYNA finite element program. In addition, Linear Regression (LR), Support Vector machines (SVM), k-Nearest Neighbors (k-NN) and Random Forest (RF) machine learning algorithms were used to predict the maximum strength values of composite pipes under impact. The peak force value of the patched specimen increased by maximum 87.2% while the energy absorption efficiency value decreased by 78.9% compared to the unpatched specimen. It was determined that the patch reduced the impact failure zone of the specimen. KNN has the highest accuracy rate with 85% in predicting PF, while RF is more reliable than other algorithms in predicting AE and MD outputs with 97% and 87%, respectively.

关键词： Composite circular tubes patch low velocity impact test machine-learning algorithms LS DYNA

来源：评论

学校读者我要写书评

暂无评论

Recent advances in the application of machine-learning algorithms to predict adsorption energies

引用

TRENDS IN CHEMISTRY 2022年第4期4卷 347-360页

作者： Cao, Liang Zhejiang Univ Dept Chem Hangzhou 321000 Zhejiang Peoples R China

Adsorption energies on the surface sites of heterogeneous catalysts, together with the Sabatier volcano plot correlating them with the reaction activation barrier (& UDelta;Ga) along the catalytic reaction pathways through Bronsted-Evans-Polanyi (BEP) relations, determine the catalytic activity. This review categorizes machine learning (ML) models into on-lattice and off-lattice models, discusses the different approaches to build models predicting adsorption energies, and summarizes several recent advances in the use of ML algorithms. These developed ML models enable researchers to rationally design high-performance heterogeneous catalysts by identifying active surface features. Furthermore, this review concludes with the limitations of current models and the challenges needing to be addressed to build sophisticated models that are more consistent with the real-life operating conditions of catalytic reactions without sacrificing speed.

关键词： machine-learning algorithms adsorption energy on-lattice models off-lattice models cluster expansions neural network

来源：评论

学校读者我要写书评

暂无评论

Comparison of different machine-learning algorithms for land use land cover mapping in a heterogenous landscape over the Eastern Nile river basin, Ethiopia

引用

ADVANCES IN SPACE RESEARCH 2024年第5期74卷 2180-2199页

作者： Yimer, Sadame M. Bouanani, Abderrazak Kumar, Navneet Tischbein, Bernhard Borgemeister, Christian Abou Bakr Belkaid Univ Tlemcen Fac Technol Dept Hydraul Lab 25 BP 119 Pole Chetouane Tilimsen 13000 Algeria Wollo Univ Dept Nat Resources Management Dessie Ethiopia Pan African Univ Inst Water & Energy Sci PAUWES Tilimsen Algeria Ctr Dev Res ZEF Dept Ecol & Nat Resources Management Bonn Germany United Nations Univ Inst Environm & Human Secur UN Global Mt Safeguard Res GLOMOS UN CampusPl Vereinten Nationen 1 D-53113 Bonn Germany

Land use/land cover (LULC) information is regarded as one of the most important variables in global change studies. Several LULC classification algorithms are available with different levels of accuracy in LULC mapping under different geographical setups. Furthermore, different remote sensing (RS) based indices and auxiliary data have been reported to have contributed to the accuracy of LULC mapping. Hence, the main aim of this study was to compare the different ML algorithms for the LULC mapping and explicitly assess the potential contribution of different RS-derived indices and auxiliary data on classification accuracy in the Upper Tekeze-Atbarah (UTA) river basin in Ethiopia. The overall classifiers evaluation result revealed that random forest (RF) performed best and followed by KNearest Neighbour (KNN), Support Vector machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), and Classification and Regression Tree (CART). Compared to RF, the MLC method showed less accuracy by 16.2% (19.7%) in OA (kappa coefficient) respectively, which showed that the currently emerging ML classifiers contributed to an improvement in LULC mapping. The use of RS-derived indices and DEM as predictors together with the Landsat bands improved the overall accuracy and kappa coefficient by 5.5% and 7% respectively. Furthermore, the discrepancy in the classified LULC map from RF and other classifiers was compared at pixel-level and the result showed a considerable disagreement between the LULC maps. For instance, when the LULC map from RF and KNN are compared, only 80.6% of the area has the same LULC condition. This result reflects the potential discrepancy that could exist in LULC maps from different classifiers. The hydrological simulation under each of the classified map also resulted in a considerable difference in the major water balance components. In a conclusion, given the considerable difference in classifier performance and the potential propagat

关键词： Land use land cover classification machine-learning algorithms Remote sensing Landsat Supervised classification Nile river basin

来源：评论

学校读者我要写书评

暂无评论

Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

引用

Virologica Sinica 2022年第3期37卷 437-444页

作者： Huiting Chen Zhaozhong Zhu Ye Qiu Xingyi Ge Heping Zheng Yousong Peng Bioinformatics Center College of BiologyHunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangsha410082China

The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune ***,there is still a lack of effective tools for determining the cleavage sites of the 3CL *** study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and *** residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in *** RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus *** achieved an AUC of 0.95 and predicted correctly 80%of the cleavage ***,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF *** proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and ***,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF ***,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.

关键词： Coronavirus 3C-like protease Cleavage sites machine-learning algorithms 3CLP webserver

来源：评论

学校读者我要写书评

暂无评论

Combining and comparing various machine-learning algorithms to improve dissolved gas analysis interpretation

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第15期12卷 3673-3679页

作者： Senoussaoui, Mohammed El Amine Brahami, Mostefa Fofana, Issouf Djilali Liabes Univ Sidi Bel Abbes Intelligent Control & Elect Power Syst Lab ICEPS Sidi Bel Abbes Algeria Univ Quebec Chicoutimi ViAHT Chicoutimi PQ Canada

Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine-learning algorithms are used for DGA interpretation. Here, four well-known machine-learning algorithms are compared in terms of DGA fault classification - Bayes network, multilayer perceptron, k-nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre-processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods.

关键词： transformer oil power engineering computing Bayes methods decision trees nearest neighbour methods pattern classification fault diagnosis multilayer perceptrons chemical engineering computing DGA fault classification classification models bootstrap aggregation Adaboost algorithm J48 decision tree k-nearest neighbour multilayer perceptron Bayes network artificial intelligence techniques Duval triangle key gases ratio method DGA interpretation dissolved gas analysis interpretation machine-learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using machine-learning algorithms

引用

ENERGIES 2022年第7期15卷 2602页

作者： Salah, Saeed Alsamamra, Husain R. Shoqeir, Jawad H. Al Quds Univ Dept Comp Sci POB 89 Jerusalem 20002 Palestine Al Quds Univ Dept Phys POB 89 Jerusalem 20002 Palestine Al Quds Univ Dept Earth & Environm Sci POB 89 Jerusalem 20002 Palestine

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R-2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site.

关键词： wind speed wind energy machine-learning algorithms artificial neural network mean absolute percentage error

来源：评论

学校读者我要写书评

暂无评论

Evaluating Combinations of Sentinel-2 Data and machine-learning algorithms for Mangrove Mapping in West Africa

引用

REMOTE SENSING 2019年第24期11卷 2928-2928页

作者： Mondal, Pinki Liu, Xue Fatoyinbo, Temilola E. Lagomasino, David Univ Delaware Dept Geog & Spatial Sci Newark DE 19716 USA Univ Delaware Dept Plant & Soil Sci Newark DE 19716 USA Columbia Univ Ctr Int Earth Sci Informat Network CIESIN Palisades NY 10964 USA Harvard Univ CGA Cambridge MA 02138 USA NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA East Carolina Univ Dept Coastal Studies Wanchese NC 27948 USA

Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827-2807 km(2) for Senegal and 245-1271 km(2) for The Gambia with one outlier for each country. We further report "Places of Agreement" (PoA) to identify areas where all iterations for both methods agree (506.6 km(2) and 129.6 km(2) for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.

关键词： mangrove machine-learning algorithms google earth engine random forest CART Senegal The Gambia Africa

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：