Research on the Photovoltaic MPPT Method Based on Improved BP-SVM-ELM Combination Prediction by DAI Bowang, ZHAO Xianggui, ZHU Qiliang

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Learning the Value of Place: Machine Learning Models for Real Estate Appraisal in Istanbul’s Diverse Urban Landscape by Ahmet Hilmi Erciyes, Toygun Atasoy, Abdurrahman Tursun, Sibel Canaz Sevgen

“…This study employs six algorithms: Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbors, Support Vector Regression, Random Forest, and Semi-Log Regression, to estimate the values of real estate on both the Asian and European continent parts of İstanbul. …”
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Robust screening of atrial fibrillation with distribution classification by Pierre-François Massiani, Lukas Haverbeck, Claas Thesing, Friedrich Solowjow, Marlo Verket, Matthias Daniel Zink, Katharina Schütt, Dirk Müller-Wieland, Nikolaus Marx, Sebastian Trimpe

“…We introduce the first distributional support vector machine (SVM) for robust detection of AF from short, noisy electrocardiograms. …”
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Eye Collateral Channel Characteristic Analysis and Identification Model Construction of Mild Cognitive Impairment by WU Tiecheng, CAO Lei, YIN Lianhua, HE Youze, LIU Zhizhen, YANG Minguang, XU Ying, WU Jinsong

“…Different MCI identification models were constructed using support vector machine, decision tree, artificial neural network and random forest algorithm, with MCI eye collateral channel characteristics and TCM syndrome elements as independent variables and onset of MCI as a dependent variable. …”
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Predicting soybean seed germination using the tetrazolium test and computer intelligence by Marcio Alves Fernandes, Izabela Cristina de Oliveira, Marcio Dias Pereira, Breno Zaratin Alves, Alan Mario Zuffo, Charline Zaratin Alves

“…The data analysis used the correlation coefficient and mean absolute error as accuracy parameters of the algorithms. The results highlighted the support vector machine as the most effective algorithm for predicting germination, with the viability and vigor + viability inputs showing the best results. …”
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Methods of Machine-Aided Training in Small Business: Content and Management by S. A. Tishchenko, M. A. Shakhmuradian

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Rolling Bearing Fault Diagnosis Based on Optimized VMD Combining Signal Features and Improved CNN by Yingyong Zou, Xingkui Zhang, Wenzhuo Zhao, Tao Liu

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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

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Characterization of defective coffee beans and blends differentiation based on 1H qNMR technique by Gui-Lin Hu, Chen-Xi Quan, Hao-Peng Dai, Ming-Hua Qiu

“…The 1H NMR from water-soluble content was shown to be more effective than that of oil fraction for qualitative of DCB blends, regardless of whether partial least squares discriminant analysis (PLS-DA) or machine learning (ML) algorithms were used. Support vector machine (SVM) was proved to be excellent for distinguishing DCB blends. …”
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An exploration of machine learning approaches for early Autism Spectrum Disorder detection by Nawshin Haque, Tania Islam, Md Erfan

“…This study explores the application of Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree, and Random Forest for predicting Autism in children and toddlers by leveraging advancements in machine learning. …”
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Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data by Robert Gutierrez, Tianshi Fang, Robert Mainwaring, Tom Reddyhoff

“…Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. …”
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Distribution Ratio Prediction of Major Components in 30%TBP/kerosene-HNO3 System Based on Machine Learning by YU Ting1, ZHANG Yinyin2, ZHANG Ruizhi3, JIN Wenlei2, LUO Yingting2, ZHU Shengfeng3, HE Hui1, YE Guoan1, GONG Helin4

“…Since the traditional mathematical model of uranium distribution ratio leads to at least 15% prediction error, in this paper, three classical machine learning models (namely, random forest, support vector regression and K-nearest neighbor) were constructed to predict the distribution ratios of uranium, plutonium, and HNO3 in the 30%TBP/kerosene-HNO3 system. …”

Dynamic neutrosophic cognitive map with improved cuckoo search algorithm (DNCM-ICSA) and ensemble classifier for rheumatoid arthritis (RA) disease by B. Chithra, R. Nedunchezhian

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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…In this work, multiple machine learning models, including linear regression (LR), support vector machine (SVM), decision tree regressor (DTR), random forest regressor (RFR), and multilayer perceptron (MLP), are used to predict the IFT of the CO2 and aqueous solution of NaCl. …”
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Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning by anfal A. Fadhil, Asmaa’ H. AL_Bayati, Ibrahim Ahmed Saleh

“…The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). …”
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Using topological data analysis and machine learning to predict customer churn by Marcel Sagming, Reolyn Heymann, Maria Vivien Visaya

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An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

“…During the calibration phase, the model was trained using three machine learning algorithms: Random forest, support vector machine, and multi-layer perceptron. …”
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Feature extraction using sparse component decomposition for face classification by Hamid Reza Shahdoosti

“…Then, the extracted features are fed to the support vector machine classifier. To evaluate the accuracy rate of the proposed method, three datasets are used. …”
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Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA by Francisca Blanco, Ye Woo

“…This paper presents a novel approach by combining a Support Vector Machine (SVM) with advanced optimization algorithms to estimate the CS of SCC mixtures accurately. …”
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A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring by Can WANG, Jianxin PENG, Xiaowen ZHANG

“…The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. …”
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