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Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and machine learning algorithms

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IEEE ACCESS 2022年 10卷 77054-77067页

作者： Soangra, Rahul Wen, Yuxin Yang, Hualin Grant-Beuttler, Marybeth Chapman Univ Fowler Sch Engn Orange CA 92866 USA Chapman Univ Crean Coll Hlth & Behav Sci Dept Phys Therapy Irvine CA 92618 USA

Idiopathic toe walking (ITW) is a gait abnormality in which children's toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of machine learning (ML) algorithms in identifying initial foot contact (heel strike versus toe strike) utilizing wearable body sensors. Thirty-six children (Age 9.4 +/- 2.8 years) diagnosed with ITW participated in this study. Six ML algorithms, consisting of Support Vector machines (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), Multi-layer Perceptron (MLP), and Gaussian process (GP), could successfully classify initial contact walking patterns among ITW. We found that a simple KNN algorithm resulted in the highest accuracy of 92.92% and an F1-score of 93.20% to differentiate toe walking gait versus best heel strike when using all four body sensors. We also found that toe walking resulted in higher variability in the sacral vertical accelerations among children diagnosed with ITW. Accurate quantification of toe walking steps in clinical applications is critical for assessing rehabilitation progress and designing new interventions for children diagnosed with ITW.

关键词： Legged locomotion Sensors Support vector machines Pediatrics Feature extraction machine learning algorithms Classification algorithms Best heel strike (BHS) gait idiopathic toe walking (ITW) inertial measurement unit (IMU) machine learning (ML)

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Temperature Prediction for Electric Vehicles Using machine learning algorithms

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第6期60卷 9251-9259页

作者： Kishore, Shradha Bharti, Sonam Kumari Anand, Priyadarshi Srivastav, Dishant Sonali, Shubham Birla Inst Technol Dept Elect & Elect Engn Patna Off Campus Patna 800014 India

The development of precise, efficient, and cost-effective temperature prediction algorithms to maintain the ambient charging and operational environment of electric vehicles (EVs) has gained significance in recent times. machine learning algorithms are widely used for prediction and decision-making processes due to the growing presence of optimized statistical models based on training datasets, that are far superior to conventional computational statistical methods. In this work, a temperature dataset has been generated in real-time from an IoT and temperature sensor-based hardware, clipped-on the casing of a vehicle, pre-possessed and fitted into optimized models so that future values of temperature can be accurately predicted using different supervised regression machine learning algorithms. The predicted temperatures have been compared with the actual recorded temperatures and a statistical error analysis has been done to compare the results based on the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-Square (R-2) and adjusted R-2 Score. The advantage of this methodology is that it is independent of system parameters, and that any future values of critically high temperature may be predicted before they occur, so that drivers can be alerted and take corrective measures before actual damage takes place. This methodology may be applied to prevent incidents of fire in EV batteries, energy wastage due to high temperatures during charging or normal running, and to enhance passenger comfort.

关键词： Temperature sensors Temperature measurement machine learning algorithms Predictive models Monitoring Batteries Prediction algorithms Regression machine learning temperature prediction random forest r-square

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Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第32期44卷 17260-17268页

作者： Ozbas, Emine Elmaslar Aksu, Dogukan Ongen, Atakan Aydin, Muhammed Ali Ozcan, H. Kurtulus Istanbul Univ Cerrahpasa Engn Fac Dept Environm Engn TR-34320 Istanbul Turkey Istanbul Univ Cerrahpasa Engn Fac Dept Comp Engn TR-34320 Istanbul Turkey

Prediction of clean hydrogen production via biomass gasification by supervised machine learning algorithms was studied. Lab-scale gasification studies were performed in a steel fixed bed updraft gasifier having a cyclone separator. Pure oxygen, and dried air with varying flow rates (0.05-0.3 L/min) were applied to produce syngas (H-2, CH4, CO). Gas compositions were monitored via on-line gas analyzer. Various regression models were created by using different machine learning (ML) algorithms which are Linear Regression (LR), K Nearest Neighbors (KNN) Regression, Support Vector machine Regression (SVMR) and Decision Tree Regression (DTR) algorithms to predict the value of H-2 concentration based on the other parameters that are time, temperature, CO, CO2, CH4, O-2 and heating value. The highest hydrogen value in syngas was found around 35% vol. after gasification experiments with higher heating value (HHV) of approximately 3400 kcal/m30.05 L/min and 0.015 L/min were the optimum flow rates for dried air and pure oxygen, respectively. In modeling section, it was observed that H2 concentrations were being reflected effectively by the concentrations estimated through the proposed model structures, and by having r(2) values of 0.99 which were ascertained between actual and model results. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： machine learning algorithms Gasification Biomass Hydrogen

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The application and comparison between machine learning algorithms in cooperative spectrum sensing

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第33期83卷 78697页

作者： Hu, Bin Liu, Yuxiang Zhai, Mingxi Wang, Aoxiang Univ Durham Dept Elect & Informat Engn Durham DH1 4FL England Wuhan Britain China Sch Wuhan 430022 Peoples R China Shenzhen Sr High Sch Shenzhen 518040 Peoples R China Wuhan Univ Technol Dept Elect & Elect Engn Wuhan 430070 Peoples R China

The world is progressing at a rapid rate, and with continuous advancements in technology, the need for cooperative spectrum sensing has evolved. Cooperative spectrum sensing is an approach used to enhance detection performance, wherein secondary users collaborate with each other to sense the spectrum and identify spectrum holes. As technology improves over time, the spectrum becomes increasingly allocated to primary users. The role of cooperative spectrum sensing in cognitive radio fulfills the essential requirement of protecting primary users from harmful interference. The progressive evolution of technology has led to a reduction in available spectrum, prompting the emergence of the concepts of cognitive radio and cooperative spectrum sensing. To begin with, this paper introduces the fundamental application of cooperative spectrum sensing algorithms, encompassing primary and secondary users' models and an energy model. Subsequently, three machine learning algorithms, namely K-means, support vector machine, and logistic regression, are explained, and their schematics are presented in detail. The proposed model uses supervised and unsupervised learning techniques to develop a cooperative spectrum sensing framework. It compares the performance of three machine learning algorithms, K-means clustering, logistics regression and support vector machine. The comparison is based on accuracy, recall and precision metrics, and the results show that the K-means clustering algorithm has better performance than the other two algorithms. The findings highlight the superiority of the K-means clustering algorithm over logistic regression and support vector machine.

关键词： Cognitive radio networks Cooperative spectrum sensing machine learning algorithms Support vector machine Logical regression k-means Accuracy Recall Precision

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Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms

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PHYSICA SCRIPTA 2024年第6期99卷 066011-066011页

作者： Tawfik, Wael Z. Abdel-Wahab, Mohamed Sh Lee, June Key Al-Enizi, Abdullah M. Youssef, Rasha Y. Beni Suef Univ Fac Sci Phys Dept Bani Suwayf 62511 Egypt Beni Suef Univ Fac Postgrad Studies Adv Sci Mat Sci & Nanotechnol Dept Bani Suwayf 62511 Egypt Chonnam Natl Univ Dept Mat Sci & Engn Gwangju 61186 South Korea King Saud Univ Coll Sci Dept Chem POB 2455 Riyadh 11451 Saudi Arabia Suez Univ Fac Comp & Informat Dept Informat Syst Suez 43512 Egypt

Carbon nanotubes (CNTs) have emerged as promising materials for supercapacitors (SCs) due to their unique properties and exceptional electrical conductivity. These cylindrical structures composed of carbon atoms offer several advantages for SC electrode applications. The electrochemical performance of CNT-based electrodes is strongly influenced by factors such as surface area, pore structure, and ID/IG ratio. However, the lack of a credible physical model capable of accurately predicting the performance of SCs based on these physicochemical properties of CNTs poses a challenge. In this study, we propose the utilization of a data-driven approach employing various models including a gradient boosting regression (GBR), Bayesian regression (BR), ridge regression (RR), and stochastic gradient descent (SGD) model to predict the performance of SCs with CNT electrodes based on the microstructural properties of the electrode material and electrochemical operational parameters. The developed GBR model demonstrates its feasibility by achieving a low root mean square error (RMSE) value of approximately 36.31 for the prediction of specific capacitance for test split. Additionally, a sensitivity analysis was conducted to investigate the influence of independent input parameters on a single output parameter, specifically the specific capacitance. This analysis provides insights into the relative importance and impact of various input parameters on the specific capacitance of CNT-based SCs.

关键词： modeling approaches capacitance prediction carbon nanotubes supercapacitors electrodes machine learning algorithms

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Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and machine learning algorithms in Four Climatic Regions

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PURE AND APPLIED GEOPHYSICS 2020年第9期177卷 4479-4508页

作者： Raza, Ali Shoaib, Muhammad Faiz, Muhammad Abrar Baig, Faisal Khan, Mudasser Muneer Ullah, Muhammad Kaleem Zubair, Muhammad Bahauddin Zakariya Univ Dept Agr Engn Multan Pakistan Northeast Agr Univ Coll Water Conservancy & Civil Engn Harbin Peoples R China Bahauddin Zakariya Univ Dept Civil Engn Multan Pakistan Univ Lahore Dept Civil Engn Lahore Pakistan NFC Inst Engn & Technol Dept Civil Engn Multan Pakistan

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95-99%;r = 0.96-1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models.

关键词： Reference evapotranspiration machine learning algorithms cross validation climatic regions

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Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms

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SOFT COMPUTING 2016年第3期20卷 1179-1189页

作者： Prasad, V. Rao, T. Srinivasa Babu, M. Surendra Prasad Raghu Inst Technol Dept CSE Visakhapatnam Andhra Pradesh India Gitam Univ Gitam Inst Technol Dept CSE Visakhapatnam Andhra Pradesh India Andhra Univ Coll Engn Autonomous Dept CS&SE Visakhapatnam Andhra Pradesh India

This work is a hybrid architecture design furnished successfully using artificial intelligence techniques, rough data sets theory and machine learning algorithms. The purpose of this work is to bring the spotless and smart approach in identifying the thyroid disease in a human. There are several mechanisms implemented on thyroid data sets which produced astonished outcomes, but the data considered for the thyroid disease diagnoses (TDD) is inconsistent, redundant and consists of missing attribute values as per my literature survey. The proposed work is to construct an expert advisory system of hybrid architecture, which is to determine the optimistic disease growth because of the thyroid gland. A string matching system (SMS) was at the outset developed, which can predict the actual TDD based on the knowledge available. If the SMS fails, an individual approaches using artificial bee colony optimization and particle swarm optimization are developed to achieve the accuracy of results appreciating the measure values as 65 and 93 %, but the results obtained using the above said approaches are calculated using some missed attribute values which are not included in the knowledge likewise left blank and hence the proposed work continues to first generate the missing attribute value in the knowledge by using rough data sets theory and the obtained data (missed attribute values) is given to predict optimistic disease along with its prevention and its curing methods. However, the data generated cannot predict the optimal disease and hence it is proposed to use a machine learning algorithm so that, obtained result is hygienic. The knowledge for implementation of this work is gathered from Intelligent System Laboratory of K. N. Toosi University of Technology, ImamKhomeini Hospital. Aquestionnaire form is developed for providing an interface for user so they can contribute the data.

关键词： machine learning algorithms Rough data sets Medical diagnose Thyroid disease expert advisory system Hybrid architecture Optimistic disease

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Exploitation of Time Series Sentinel-2 Data and Different machine learning algorithms for Detailed Tree Species Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 7589-7603页

作者： Xi, Yanbiao Ren, Chunying Tian, Qingjiu Ren, Yongxing Dong, Xinyu Zhang, Zhichao Nanjing Univ Int Inst Earth Syst Sci Nanjing 210023 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Peoples R China Jilin Univ Coll Earth Sci Changchun 130100 Peoples R China

The classification of tree species through remote sensing data is of great significance to monitoring forest disturbances, biodiversity assessment, and carbon estimation. The dense time series and a wide swath of Sentinel-2 data provided the opportunity to map tree species accurately and in a timely manner over a large area. Many current studies have applied machine learning (ML) algorithms combined with Sentinel-2 images to classify tree species, but it is still unclear, which algorithm is more effective in the automotive extraction of tree species. In this study, five ML algorithms were compared to identify the composition of tree species with multitemporal Sentinel-2 images in the JianShe forest farm, Northeast China. Three major types of deep neural networks [Conv1D, AlexNet, and long short-term memory (LSTM)] were tested to classify Sentinel-2 time series, which represent three disparate but effective strategies to apply sequential data. The other two models are support vector machine (SVM) and random forest (RF), which are renowned for extensive adoption and high performance for various remote sensing applications. The results show that the overall accuracy of neural network models is better than that of SVM and RF. The Conv1D model had the highest classification accuracy (84.19%), followed by the LSTM model (81.52%), and the AlexNet model (76.02%). For non-neural network models, RF's classification accuracy (79.04%) is higher than that of SVM (72.79%), but lower than that of Conv1D and LSTM. Therefore, the deep neural networks combined with multitemporal Sentinel-2 images can efficiently improve the accuracy of tree species classification.

关键词： Vegetation Forestry Support vector machines machine learning algorithms Time series analysis Remote sensing Feature extraction Deep learning sentinel-2 image sequential pattern tree species classification

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Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 2569-2582页

作者： Sadrossadat, Ehsan Basarir, Hakan Karrech, Ali Elchalakani, Mohamed Univ Western Australia Dept Civil Environm & Min Engn Crawley WA 6009 Australia Norwegian Univ Sci & Technol Dept Petr & Geosci Trondheim Norway

Ultra-high-performance concrete (UHPC) is a recent class of concrete with improved durability, rheological and mechanical and durability properties compared to traditional concrete. The production cost of UHPC is considerably high due to a large amount of cement used, and also the high price of other required constituents such as quartz powder, silica fume, fibres and superplasticisers. To achieve specific requirements such as desired production cost, strength and flowability, the proportions of UHPC's constituents must be well adjusted. The traditional mixture design of concrete requires cumbersome, costly and extensive experimental program. Therefore, mathematical optimisation, design of experiments (DOE) and statistical mixture design (SMD) methods have been used in recent years, particularly for meeting multiple objectives. In traditional methods, simple regression models such as multiple linear regression models are used as objective functions according to the requirements. Once the model is constructed, mathematical programming and simplex algorithms are usually used to find optimal solutions. However, a more flexible procedure enabling the use of high accuracy nonlinear models and defining different scenarios for multi-objective mixture design is required, particularly when it comes to data which are not well structured to fit simple regression models such as multiple linear regression. This paper aims to demonstrate a procedure integrating machine learning (ML) algorithms such as Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) to develop high-accuracy models, and a metaheuristic optimisation algorithm called Particle Swarm Optimisation (PSO) algorithm for multi-objective mixture design and optimisation of UHPC reinforced with steel fibers. A reliable experimental dataset is used to develop the models and to justify the final results. The comparison of the obtained results with the experimental results validates the capability of the p

关键词： Ultra-high-performance concrete Mixture design Multi-objective optimisation machine learning algorithms Particle swarm optimisation

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Automated classification of hand-woven and machine-woven carpets based on morphological features using machine learning algorithms

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JOURNAL OF THE TEXTILE INSTITUTE 2024年第12期115卷 2650-2659页

作者： Isik, Melike Ozulku, Burcu Kursun, Ramazan Taspinar, Yavuz Selim Cinar, Ilkay Yasin, Elham Tahsin Koklu, Murat Selcuk Univ Dept Technol Selcuklu Turkiye Selcuk Univ Guneysinir Vocat Sch Guneysinir Turkiye Selcuk Univ Doganhisar Vocat Sch Doganhisar Turkiye Selcuk Univ Dept Comp Engn Selcuklu Turkiye Selcuk Univ Grad Sch Nat & Appl Sci Selcuklu Turkiye

As a cultural heritage, hand-woven carpets engender trust and admiration in individuals who recognize their authenticity. It is the expertise of experts who determine whether a carpet is hand-woven or machine-woven based on authenticity criteria. A total of 48 morphological features were extracted by three carpet experts from 359 handwoven and machine woven carpets. machine-learning algorithms were used to classify the extracted features. With an accuracy of 96.66%, the ANN algorithm achieved the best classification performance. Afterward, 28 morphological features were selected with the highest gain ratios and reclassified. Based on the selected features, SVM (Support Vector machine) achieved the best classification accuracy of 96.66%. A carpet expert performed classification using the morphological features extracted by machine learning algorithms to evaluate the classification results obtained through machine learning algorithms. When 48 morphological features were used, the accuracy was 98.61%, and when 28 morphological features were used, the accuracy was 97.77%. Based on the results, artificial intelligence techniques are suitable for detecting morphological features in hand-woven and machine-woven carpets and for automatically classifying them.

关键词： Hand-woven carpet machine-woven carpet carpet classification machine learning algorithms morphological features of carpets

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