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Comparative investigation of the usability of different machine learning algorithms in the analysis of battery thermal performances of electric vehicles

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第15期46卷 21104-21126页

作者： Colak, Andac Batur Nigde Omer Halisdemir Univ Engn Fac Dept Mech Engn TR-51240 Nigde Turkey

The cooling and thermal management of battery packs, which are the most important components used in electric vehicles (EV), are of critical importance for the efficiency and performance of EV. This study aims to analyze the usability of machine learning algorithms in determining the thermal parameters of the battery thermal management system (BTMS) used in EV and to determine the machine learning algorithm with the highest prediction performance. The prediction performance of three different artificial neural networks developed by using Levenberg-Marquardt, Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) machine learning algorithms have been extensively and comparatively investigated. As input parameters of the models, discharge rate, flow rate, and inlet temperature values were defined and the average temperature of the battery surface and maximum temperature difference on the surface values were estimated. The coefficient of determination values for the Levenberg-Marquardt, BR, and SCG algorithms was calculated as 0.99848, 0.98751, and 0.97592, respectively. The results showed that the machine learning algorithms can determine the thermal parameters of the BTMS of EV with high accuracy. However, it has been observed that the highest prediction accuracy belongs to the Levenberg-Marquardt algorithm.

关键词： artificial neural network battery battery thermal management system electric vehicles machine learning algorithms

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Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 1-9页

作者： Yuvaraj, N. SriPreethaa, K. R. KPR Inst Engn & Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Health care systems are merely designed to meet the needs of increasing population globally. People around the globe are affected with different types of deadliest diseases. Among the different types of commonly existing diseases, diabetes is a major cause of blindness, kidney failure, heart attacks, etc. Health care monitoring systems for different diseases and symptoms are available all around the world. The rapid development in the fields of Information and Communication Technologies made remarkable improvements in health care systems. Various machine learning algorithms are proposed which automates the working model of health care systems and enhances the accuracy of disease prediction. Hadoop cluster based distributed computing framework supports in efficient processing and storing of extremely large datasets in cloud environment. This work proposes the novel implementation of machine learning algorithms in hadoop based clusters for diabetes prediction. The results show that the machine learning algorithms can able to produce highly accurate diabetes predictive healthcare systems. Pima Indians Diabetes Database from National Institute of Diabetes and Digestive Diseases is used to evaluate the working of algorithm.

关键词： Healthcare systems machine learning algorithms Hadoop clusters Predictive analysis Cluster computing

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Utilizing machine learning algorithms to Predict Accuracy of the Index of Relative Tectonic Activity (IRTA), Dhansiri (North) River Basin in India and Bhutan

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IEEE ACCESS 2024年 12卷 60482-60495页

作者： Roy, Shayani Mandal, Pritam Chowdhury, Amitava Abdullah-Al-Wadud, M. Seikh, Ariyan H. Seikh, Ayan H. Ghosh, Manojit Mukhopadhyay, Ananya Indian Inst Engn Sci & Technol IIEST Dept Earth Sci Howrah 711103 India Indian Inst Engn Sci & Technol IIEST Dept Met & Mat Engn Howrah 711103 India Pandit Deendayal Energy Univ Dept Comp Sci & Engn Gandhinagar 382007 India King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Motilal Nehru Natl Inst Technol Allahabad Prayagra Dept Comp Sci & Engn Prayagraj 211004 India

A river tries to maintain a dynamic equilibrium state by adjusting different controlling factors. A significant change in one of the controlling factors will dictate modifications in the others to re-establish the equilibrium in a river system. A river basin may indicate active tectonic movements more precisely than the best space-based geodetic techniques. Morphometric analyses, with the help of DEM and GIS often generates insights into the tectonic activities of an area. The Dhansiri (North) River basin lies on the north bank of the Brahmaputra and on the northern part of the Dhansiri-Kopili fault, which is tectonically active at different times. This paper analyses the impact of relative tectonics on drainage pattern development in the basin based on various morphometric parameters of linear (stream length ratio, bifurcation ratio), areal (form factor, basin elongation ratio), and relief (relief ratio, ruggedness number) aspects. Eleven well-known ML algorithms,namely, Logistic Regression (LR), K Nearest Neighbors (KNN), Random Forest (RF), Support Vector machine (SVM), Decision Tree (DT), Gaussian Naive Bayes (GNB) classifier, Neural Network (NN), Extra Tree Classifier (ET), Ada Boost Classifier (AB), Gradient Boosting Classifier (GB), XG Boost Classifier (XGB) is used to model the spatial distribution of relative tectonic *** algorithms were executed in Python to assess prediction accuracy using standard metrics like accuracy, precision, recall, and F1 score. The assessment utilized widely used libraries such as sci-kit-learn and TensorFlow to implement and test the algorithms, benefiting from their comprehensive model evaluation and performance assessment tools. The SVM, ET, DT, and GNB techniques had the best performance, achieving an accuracy of 82.60 percent as per the modeling *** Dhansiri (North) is a sixth-seven-ordered basin characterized by a dendritic drainage pattern. Notably,the spatial prediction of morphometric parameters with M

关键词： Morphometric parameters index of relative tectonic activity tectonics Dhansiri (North) river basin machine learning algorithms

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A Wearable Sensor for Arterial Stiffness Monitoring Based on machine learning algorithms

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IEEE SENSORS JOURNAL 2019年第4期19卷 1426-1434页

作者： Miao, Fen Wang, Xurong Yin, Liyan Li, Ye Chinese Acad Sci Key Lab Hlth Informat Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China Northeastern Univ Shenyang Shenyang 110819 Liaoning Peoples R China Chinese Acad Sci Joint Engn Res Ctr Hlth Big Data Intelligent Anal Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China

Arterial stiffness is strongly associated with cardiovascular events. Existing devices for evaluating arterial stiffness based on ultrasound or pulse wave velocity suffer a lot from complexity and inconvenience in home-care settings. This paper proposed a wearable sensor for arterial stiffness monitoring via machine learning techniques. The proposed sensor is comprised of one electrocardiogram (ECG) and one photoplethysmogram (PPG) module. The ECG and PPG signals were first simultaneously collected by the wearable sensor, and 21 features were extracted from two signals for arterial stiffness evaluation. A genetic algorithm-based feature selection method was then used to select the important indicators. Multivariate linear regression (MLR), decision tree, and back propagation (BP) neural network were employed to develop the model. Vascular age and 10-year cardiovascular disease risk from OMRON arteriosclerosis instrument were deemed as the gold standard to evaluate arterial stiffness. Experimental results based on 501 diverse subjects showed that the MLR approach exhibited the best accuracy in vascular age estimation (correlation coefficient, 0.89;mean of the residual, 0.2136;and standard deviation of the residual, 6.2432). While the BP neural networks-based approach was hest in cardiovascular disease risk estimation (correlation coefficient, 0.9488;mean of the residual, - 0.3579%;and standard deviation of the residual, 3.7131%). The results indicate that the proposed learning-based sensor has great potential in arterial stiffness monitoring in home-care settings.

关键词： Arterial stiffness machine learning algorithms pulse wave analysis

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Prediction of air compressor condition using vibration signals and machine learning algorithms

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JOURNAL OF VIBRATION AND CONTROL 2023年第5-6期29卷 1342-1351页

作者： Aravinth, S. Sugumaran, V Vellore Inst Technol Sch Mech Engn Chennai CampusVandalur Kelambakkam Rd Chennai 600127 Tamil Nadu India

Air compressors are widely used equipment in the modern world for their tremendous utilization in applications of both domestic and industrial sectors. The inbuilt mechanical parts are often prone to various failures due to the complexity in the construction of air compressors that affects the overall system process. Hence, it is essential to devise a methodology to identify the failures at the early stages of its operation to avoid the major causalities due to process breakdown and system seizure. In this study, a single-acting single-stage reciprocating air compressor was chosen. The fault conditions like inlet valve fluttering, outlet valve fluttering, valve plate leakage, and check valve fault were considered. The statistical, histogram and autoregressive moving average features were extracted from the raw vibration signals. The most dominating features were selected using a decision tree algorithm and those features were classified using machine learning classifiers like Lazy K Star, Decorate, and radial basis function networks. The classifier Lazy K Star on autoregressive moving average feature exhibits the highest fault classification rate of 99.67% in classifying various compressor conditions and the results were compared and presented.

关键词： Air compressor condition monitoring fault diagnosis machine learning algorithms vibration signals

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A Systematic Literature Review on machine learning algorithms for Human Status Detection

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IEEE ACCESS 2022年 10卷 74366-74382页

作者： Sardar, Suman Kalyan Kumar, Naveen Lee, Seul Chan Gyeongsang Natl Univ Dept Ind & Syst Engn Jinju 52828 South Korea

Human status detection (HSD) is important to understand the status of users when interacting with various systems under different conditions. Recently, although various machine learning algorithms have been applied to analyze and detect human status, there are no guidelines to utilize machine learning algorithms to analyze physical, cognitive, and emotional aspects of human status. Therefore, this study aimed to investigate measures, tools, and machine learning algorithms for HSD by applying a systematic literature review method. We followed the preferred reporting items for systematic reviews and meta-analysis (PRISMA) model to answer three research questions related to the research objective. A total of 76 articles were identified using two hundred keyword combinations addressing topics under HSD in the fields of human factors and human-computer interaction (HCI). The results showed that research on HSD becomes important in industrial systems, focusing on how intelligent systems based on machine learning (ML) differ from earlier generations of automated systems, and what these differences necessarily imply for HCI to design and evaluation. The tools used to collect data for HSD on different parameters are broadly discussed. Recent HSD studies seem to focus on cognitive load and emotion, whereas prior studies have focused on the detection of physical effort. This research assists domain researchers in identifying HSD approaches using different ML algorithms that are suitable for use in their research.

关键词： machine learning algorithms Feature extraction Data acquisition Human computer interaction Electromyography Functional magnetic resonance imaging Electrocardiography Human status detection physical status cognitive status emotional status machine learning algorithms

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Fuzzy mathematics and machine learning algorithms application in educational quality evaluation model

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5583-5593页

作者： Wang, Jian Zhang, Weizhong Quzhou Coll Technol Quzhou Peoples R China Zhejiang Normal Univ Jinhua Zhejiang Peoples R China

Teaching quality evaluation is a complex non-linear system fitting problem under the influence of many factors. The establishment of teaching quality evaluation is to construct a functional relationship between teaching quality evaluation index and teaching effect. In this paper, the authors analyze the fuzzy mathematics and machine learning algorithms application in educational quality evaluation model. machine learning method has been well applied in complex problems such as classification, fitting, pattern recognition and so on. It can be used to realize a more comprehensive, reasonable and effective evaluation of the classroom teaching quality of university teachers. The simulation results show that the model can well express the complex relationship between the teaching quality evaluation index and the evaluation results. The theoretical values of the evaluation results are in the corresponding confidence interval, which proves that the machine learning algorithm has good reliability for different teaching quality evaluation problems.

关键词： Fuzzy mathematics precision contrast machine learning algorithms education quality

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Forecasting US movies box office performances in Turkey using machine learning algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 6579-6590页

作者： Cagliyora, Sandy Oztaysi, Briar Sezgin, Selime Kadir Has Univ Dept Business Adm Kadir Has Str TR-34083 Istanbul Turkey Istanbul Tech Univ Dept Ind Engn Macka Istanbul Turkey Bilgi Univ Dept Business Adm Istanbul Turkey

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

关键词： machine learning algorithms motion picture industry forecasting

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Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms

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International Journal of Digital Earth 2023年第1期16卷 3706-3724页

作者： Hengxing Xiang Yanbiao Xi Dehua Mao Tianyuan Xu Ming Wang Fudong Yu Kaidong Feng Zongming Wang State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunPeople’s Republic of China International Institute for Earth System Science Nanjing UniversityNanjingPeople’s Republic of China School of Foreign Languages Zhongnan University of Economics and LawWuhanPeople’s Republic of China College of Information and Technology Jilin Agricultural UniversityChangchunPeople’s Republic of China National Earth System Science Data Center BeijingPeople’s Republic of China

Climate change and human activities have reduced the area and degraded the functions and services of wetlands in *** protect and restore wetlands,it is urgent to predict the spatial distribution of potential *** this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning *** on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning *** accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of *** area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast *** features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.

关键词： Potential wetland distribution machine learning algorithms geographic big data China wetland geographic features

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RA-ECDM of Silicon Wafers Using Taguchi's Methodology and machine learning algorithms

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SILICON 2023年第3期15卷 1511-1526页

作者： Singh, Manpreet Antil, Parvesh Singh, Sarbjit Katal, Nitish Bakshi, Dapinder Kaur Alkesh Punjab State Council Sci & Technol Chandigarh India CCS HAU Coll Agr Engn & Technol Dept Basic Engn Hisar Haryana India Punjab Engn Coll Dept Mech Engn Chandigarh India Indian Inst Informat Technol Sch Elect Una Himachal Prades India

As a prominent machining process, electrochemical discharge machining (ECDM) is used to process materials that are both fragile and difficult to cut. The growing use of this method for the machining of hard to cut materials has paved the way for the discovery of unexploited potential in machining outcomes. In the current study, silicon wafers were machined by rotary assisted electrochemical discharge machining using Taguchi's L-27 orthogonal array, having output process characteristics such as overcut and hole circularity. The optimization through Taguchi's methodology shows that the overcut and circularity of holes improved by 1.40% and 2.44% by using optimal parametric combination as compared to orthogonal array. All output process parameters were again analysed through a machine learning algorithm by determining their root mean squared error (RMSE), R-Squared, mean squared error (MSE), mean absolute error (MAE), prediction speed, and training time. The obtained results show that in regression models of the Gaussian process, overcut and circularity of hole are well predicted by both the exponential and squared exponential regression models. The overcut and circularity of hole models had R-squared values of 0.92 and 0.90, respectively.

关键词： machine learning algorithms RA-ECDM Silicon Wafers Taguchi's Methodology

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