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Estimating the compressive strength of plastic concrete samples using machine learning algorithms

Asian Journal of Civil Engineering 2024年第2期25卷 1503-1516页

作者： Alishvandi, Ali Karimi, Javad Damari, Siavash Moayedi Far, Arsham Setodeh Pour, Mohammad Ahmadi, Morteza Rock Mechanics Division School of Engineering Tarbiat Modares University Tehran Iran Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Civil Engineering Department School of Engineering Islamic Azad University Larestan Iran

Determining the mechanical properties of plastic concrete samples through experimental investigation is costly and time-consuming. This research used supervised machine learning (ML) techniques, including Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boost (XGBoost), Support Vector machine (SVM), and K-Nearest Neighborhood (KNN) for predicting the compressive strength of the plastic concrete samples considering different values of cement, water, water-to-cement ratio, bentonite, temperature, and sand. The models' performances are compared and evaluated using the correlation of coefficient (R ²) score, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). According to the results, the DT model was more effective in predicting with R ² = 0.87. In addition, a sensitivity analysis was carried out to determine each parameter's contribution level in implementing models using the RF algorithm. Consequently, it was shown that ML techniques are valuable tools for predicting the mechanical properties of plastic concrete in a more time and cost-effective way compared to laboratory tests. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Compressive strength machine learning algorithm Plastic concrete Regression

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Comparison of the effectiveness of Extended Kalman Filter and machine learning algorithms in sensor fusion 24

Comparison of the effectiveness of Extended Kalman Filter an...

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4th International Conference on Computer, Internet of Things and Control Engineering

作者： Wang, Yijie Wuhan Univ Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9798400711848

On account of the wide application of the autonomous vehicle system, accurate and reliable location information is of great significance. Since the GPS and the IMU information doesn't perform well on their own respectively, however when merging the GPS and the IMU data from the sensors, the high precision of the navigation system is achieved. While the Extended Kalman Filter is the most prevalent in fusing the data, in this paper, algorithms of machine learning, the Random Forest Regression, the Decision Tree Regression, the Multiple Linear Regression and the Extreme Gradient Boosting for instance, algorithms of neural network, the Multiple Perceptron Neural Network for instance is applied for sensor fusion. The results from the experiment demonstrate a promising direction for machine learning and neural network in application in the sensor merging field.

关键词： Autonomous vehicle navigation system Sensor fusion Extended Kalman Filter machine learning algorithm Neural network

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RETRACTED: Performance analysis of machine learning algorithms in heart disease prediction (Retracted Article)

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2022年第4期30卷 335-343页

作者： Dhasaradhan, K. Jaichandran, R. Vinayaka Missions Res Fdn Deemed Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor Tamil Nadu India

This work presents performance analysis of machine learning algorithms such as logistic regression, naive bayes, decision tree, k nearest neighbour, random forest, support vector machine, and extreme gradient boosting in heart disease prediction. machine learning algorithms are implemented in python using Scikit learn library in Jupiter notebook. Experiments are conducted by training and testing machine learning algorithms using kaggle heart disease dataset under six test cases. Performance of machine learning algorithms are evaluated using accuracy, precision, recall, F1 score and ROC as metrics. Results show random forest reported high accuracy, precision, recall, F1 score and ROC in heart disease prediction compared to other machine learning algorithms in all six test cases. Results show RF is effective in heart disease prediction in Case 3 with 80% train data and 20% test data.

关键词： heart disease prediction machine learning algorithm random forest algorithm train data and test data

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Reduction of satellite images size in 5G networks using machine learning algorithms

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IET COMMUNICATIONS 2022年第5期16卷 584-591页

作者： Moorthy, Talari Venkata Krishna Budati, Anil Kumar Kautish, Sandeep Goyal, S. B. Prasad, Kolalapudi Lakshmi Sasi Inst Technol & Engn Tadepalligudem Andhra Pradesh India Gokaraju Rangaraju Inst Engn & Technol Dept Elect & Commun Engn Hyderabad Telangana India Lord Buddha Educ Fdn Campus Kathmandu Nepal City Univ Malaysia Petaling Jaya Malaysia BV Raju Inst Technol Narasapur Telangana India

The high data volume of multispectral satellite images is compressed for better visual perception without loss of image and statistical properties of the local or global image to provide superior information for obtaining effective results using 5G networks. This compression is a technique applied to remote sensing applications to analyse the data for prediction or forecasting the real-time applications by remote sensing applications like IoT and data transmission over 5G wireless networks. The extensive data images have multiple bands, which contain earth surface/object information with various frequencies. It is difficult to handle this extensive data for processing data. The compression is mandatory to avoid this complexity by removing redundancy data, unnecessary pixel information and non-visual redundancy data between bands. There are various standard compression techniques are available like JPEG 2000, Wavelet and DCT methods. The proposed method is implemented with a combination of intra coding and machine learning algorithm. The standard compression technique does not give better results due to degradation of pixels, lack of spatial and spectral information. This paper enriches progressive results by reduced satellite images for transmission of data in IoT and 5G wireless networks, in which qualitative results are compared by standard compression technique with suitable parameters.

关键词： remote sensing multispectral satellite images visual perception nonvisual redundancy data 5G wireless networks learning (artificial intelligence) high data volume data transmission global image spatial information geophysical image processing standard compression technique spectral information local image 5G networks using machine learning algorithms intra coding superior information unnecessary pixel information effective results image coding remote sensing applications extensive data images statistical properties multiple bands 5G mobile communication data compression satellite images size IoT reduced satellite images machine learning algorithm discrete cosine transforms real-time applications

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Fault Diagnosis and Simulation Testing of Electrical Equipment Based on machine learning algorithms 4th

Fault Diagnosis and Simulation Testing of Electrical Equipme...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Li, Chenyu School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232000 China

ISBN: (纸本)9789819778867

Electrical equipment and the stable operation of the power system have a close connection;qualified electrical equipment can ensure the normal operation of the power system. The electrical fault factors are numerous, and it is necessary to carry out early discovery and early resolution of electrical faults. Based on this, this paper analyzes an electrical equipment fault diagnosis system using machine learning algorithms, and at the same time conducts simulation experiments on the performance of the system, and its results show that the diagnosis system proposed in this paper can shorten the detection time and improve the detection accuracy. It is expected that the research in this paper can provide a corresponding basis for the efficient diagnosis of electrical equipment faults. © The Author(s) 2025.

关键词： Electrical equipment Fault diagnosis machine learning algorithm

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Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms

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Journal of Innovative Optical Health Sciences 2022年第3期15卷 23-35页

作者： Juan Zhang Yiping Liu Hongxiao Li Shisheng Cao Xin Li Huijuan Yin Ying Li Xiaoxi Dong Xu Zhang School of Dentistry Tianjin Medical University 12 Qixiangtai Road Heping District Tianjin 300070P.R.China Chinese Academy of Medical Science&Peking Union Medical College Institute of Biomedical Engineering 236 Baidi Road Tianjin 300192P.R.China

Periodontitis is closely related to many systemic diseases linked by different periodontal *** unravel the relationship between periodontitis and systemic diseases,it is very important to correctly discriminate major periodontal *** realize convenient,effcient,and high-accuracy bacterial species classification,the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis(Pg),Fusobacterium nucleatum(Fn),and Aggregatibacter actinomycetemcomitans(Aa).The result shows that this novel method can successfully discriminate the three abovementioned periodontal ***,the classification accuracies for the three categories of the original data were 94.7%at the sample level and 93.9%at the spectrum level by the machine learning algorithm extra *** study provides a fast,simple,and accurate method which is very beneficial to differentiate periodontal pathogens.

关键词： Raman spectroscopy periodontal pathogen machine learning algorithm discrimination

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GIS-based machine learning algorithms for mapping beach placer deposits in the southwest coast of India using Landsat-8 OLI images

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JOURNAL OF APPLIED REMOTE SENSING 2022年第1期16卷

作者： Rejith, Rajan Girija Sundararajan, Mayappan Gnanappazham, Lakshmanan Seenipandi, Kaliraj Ramaswamy, Sreekantaiyer CSIR Natl Inst Interdisciplinary Sci & Technol Mat Sci & Technol Div Thiruvananthapuram Kerala India Acad Sci & Innovat Res Ghaziabad India Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram Kerala India Govt India Natl Ctr Earth Sci Studies Minist Earth Sci Thiruvananthapuram Kerala India

Beach placer deposits are economically enriched with Fe-Ti oxide, Zr, and rare earth minerals such as garnet, rutile, zircon, monazite, ilmenite, and sillimanite. Landsat 8-OLI image provides the distinct spectral signature of placer deposits in its multispectral bands that can be used as a key source for mapping placer mineral deposits. GIS-based machine learning algorithms (MLAs) are used for executing the subpixel-based classification of high-dimensional data products (multispectral images, i.e., Landsat 8-OLI image) based on its spectral signatures for mapping the placer mineral deposits. The Landsat 8-OLI images are executed using the four widely used MLAs such as maximum likelihood classification, random forest classifier (RFC), support vector machine, and artificial neural network for mapping placer mineral deposits in the southwest coastal stretches of Thiruvananthapuram district in the Kerala state of India. The resultant map shows the occurrence of placer deposits with a major concentration of ilmenite. The accuracy assessment reveals that RFC shows better performance for mapping beach minerals with an overall accuracy of 84.4925% and kappa coefficient of 0.8184. Our study proves that the MLAs are the vital tools for mapping the placer mineral deposits in the sandy beaches, and thereby, this approach is being used as a scientific key tool for opening the gateways for beach mineral mapping using multispectral satellite images. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： beach placer deposits heavy minerals machine learning algorithm random forest classifier remote sensing and GIS southern coast of India

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machine learning algorithms in wood ash-cement-Nano TiO2-based mortar subjected to elevated temperatures

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RESULTS IN ENGINEERING 2023年 18卷

作者： Raheem, Akeem Ikotun, Bolanle Oyebisi, Solomon Ede, Anthony Univ South Africa Dept Civil Engn ZA-1710 Johannesburg South Africa Ladoke Akintola Univ Technol Dept Civil Engn Ogbomosho Nigeria Covenant Univ Dept Civil Engn Ota Nigeria

Mortar is subjected to high temperatures during fire attacks or when it is near heat-radiating equipment like furnaces and reactors. The physical and microstructure of mortar were considerably altered by high temperatures. In this investigation, the effects of elevated temperatures on the flexural and compressive strengths of wood ash (WA) cement mortar modified with green-synthesised Nano titanium oxide (NT) were examined. In order to produce mortar samples, the cement was replaced with 10% WA, and 1-3% NT by weight of binder were added at constant water-binder ratio. The specimens were heated to 105, 200, 400, 600, and 800? with an incremental rate of 10 ? per min in the electric furnace for a sustained period of 2 h to measure their strengths. The machine learning algorithm of artificial neural networks with Levenberg-Marquardt backpropagation training techniques of different network architectures was engaged to predict the compressive strength of WAcement-NT-based mortar produced. The findings showed that higher temperatures reduced compressive strength after 400 ? and flexural strength after 200 ?. The mortar specimen with a 3% NT addition showed the highest residual compressive strength increase, ranging from 18.75 to 27.38%. Compared to compressive strength, flexural strength is more severely affected by high temperatures. The backpropagation training algorithm revealed that each hidden layer displayed its unique strong prediction. However, Levenberg-Marquardt backpropagation training technique of 7-10-10-1 network structures yielded the best performance metrics for training, validation, and testing compared to 7-10-10-10 and 7-10-1 network architectures.

关键词： Cleaner production Compressive strength Elevated temperature Green -synthesised nano titanium machine learning algorithm Waste valorization

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Diagnosis and Classification of the Diabetes Using machine learning algorithms

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SN Computer Science 2023年第1期4卷 72页

作者： Theerthagiri, Prasannavenkatesan Ruby, A. Usha Vidya, J. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru Bengaluru India School of Computing Science and Engineering VIT Bhopal University Bhopal India

Diabetes mellitus is characterized as a chronic disease that may cause many complications. machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. This paper investigates traditional classification algorithms and neural network-based machine learning for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false-positive and false-negative rates with the highest area under the curve of 86%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification of diabetes Diabetes prediction machine learning algorithm MLP

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Analysis of Credit Card Fraud Transaction Detection using machine learning algorithms 6

Analysis of Credit Card Fraud Transaction Detection using Ma...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sahu, Shashank Sahu, Neeta Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Ajay Kumar Garg Institute of Management Ghaziabad India

ISBN: (纸本)9798350304480

Credit cards are now used in daily life. The need for online business transactions is rising as quickly as the internet. Markets are expanding thanks to the internet, which influences customers to buy the things they need. The inability to raise enough funds can occasionally prevent you from getting the things you need. For a limited time, businesses are also offering good discounts on their products. It forces the user to need to buy things. Use of credit card is necessary to satisfy the user needs. Additionally, credit card companies provide numerous discounts and reward points for using credit cards to make purchases. When the number of people increases in using credit cards, then the chance of fraudulent transaction is also increases. Finding out about fraudulent credit card transactions becomes urgently necessary. Unauthorised and illegal actions are seriously hindering the expansion of the credit card industry. Financial losses result from fraud in many circumstances. Credit cards are utilised on websites. Numerous methods are offered by machine learning algorithms which can be used for determining that whether a transaction is fraud or not. This study compares various machine learning algorithms and demonstrates that the KNN method performs best, with a 99.9% accuracy rate. This provides a thorough review of the effectiveness of various machine learning methods, which is helpful in creating applications for detecting credit card fraud. This paper also shows that lowest accuracy of 98.6% is achieved using Naive Bayes algorithm. © 2023 IEEE.

关键词： Accuracy Credit Card Fraud KNN machine learning algorithm

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