Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing by Artur Samojluk, Piotr Artiemjew

“…An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. …”
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A low-cost autonomous portable poultry egg freshness machine using majority voting-based ensemble machine learning classifiers by Jirayut Hansot, Wongsakorn Wongsaroj, Thaksin Sangsuwan, Natee Thong-un

“…The height and width of the yolk were pictured by two cameras to classify egg freshness. The proposed machine learning model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Decision Trees, Support Vector Machine, Naïve Bayes, k-Nearest Neighbors and Logical Regression to make a final prediction. …”
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Improvement of signal detection based on using machine learning by Bassam Abd

“…The proposed system depends on the support vector machine (SVM). The SVM is one of the most popular learning algorithms in different fields, such as signal processing, image processing, communication, and pattern recognition. …”
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Cognitive Radio for Smart Grid: Theory, Algorithms, and Security by Raghuram Ranganathan, Robert Qiu, Zhen Hu, Shujie Hou, Marbin Pazos-Revilla, Gang Zheng, Zhe Chen, Nan Guo

“…From the power system point of view, a supervised learning method called support vector machine (SVM) is used for the automated classification of power system disturbances. …”
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Pistachio Classification Based on Acoustic Systems and Machine Learning by Yavuz Türkay, Zekiye Seyma Tamay

“…These features are given as input to a support vector machine algorithm called FITCSVM for classification. …”
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Design and analysis of quantum machine learning: a survey by Linshu Chen, Tao Li, Yuxiang Chen, Xiaoyan Chen, Marcin Wozniak, Neal Xiong, Wei Liang

“…Secondly, we in-depth discuss 5 quantum machine learning algorithms of quantum support vector machine, quantum neural network, quantum k-nearest neighbour, quantum principal component analysis and quantum k-Means algorithm. …”
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Web application firewall based on machine learning models by Muhammed Ersin Durmuşkaya, Selim Bayraklı

“…The study evaluated five classification algorithms—K-nearest neighbors, logistic regression, naïve Bayes, support vector machine, and decision tree—for detecting cross site scripting (XSS), Structured Query Language (SQL) Injection, Operating System Command Injection, and Local File Inclusion attacks. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…This research employs advanced machine learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms, in conjunction with time-domain and frequency-domain feature extraction methods. …”
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Housing Value Forecasting Based on Machine Learning Methods by Jingyi Mu, Fang Wu, Aihua Zhang

“…In this paper, support vector machine (SVM), least squares support vector machine (LSSVM), and partial least squares (PLS) methods are used to forecast the home values. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…We compared Extreme Gradient Boosting, Support Vector Regression (SVR) and Random Forest algorithms to assess community structure based on FP raw data. …”
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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…Results: The random forest regressor (MAE: 0.416 mSv; MAPE: 7%; and R²: 0.98) showed best performances over the neural network and the support vector machine. However, all three machine learning algorithms outperformed effective dose estimation using k-factors (MAE: 2.06; MAPE: 26%) or multiple linear regression (MAE: 0.98; MAPE: 44.4%). …”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“…In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. …”
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Detecting Textual Propaganda Using Machine Learning Techniques by Akib Mohi Ud Din Khanday, Qamar Rayees Khan, Syed Tanzeel Rabani

“…The relevant features are supplied to support vector machine (SVM) and Multinomial Naïve Bayesian (MNB) classifiers. …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…While advanced classification algorithms, including support vector machines (SVMs), multilayer perceptrons (MLPs), and convolutional neural networks (CNNs), have demonstrated near-perfect accuracy (approaching 100%), their high training times limit their widespread applicability across institutions. …”
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Advances in machine learning for the detection and characterization of microplastics in the environment by M. Maksuda Khanam, M. Khabir Uddin, Julhash U. Kazi

“…In particular, algorithms such as support vector machines, random forests, and convolutional neural networks have demonstrated considerable success in classifying microplastics based on chemical signatures and visual characteristics. …”
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Data-Driven Computational Methods in Fuel Combustion: A Review of Applications by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…The most frequently applied methods include artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs) for predictive modeling, as well as genetic algorithms (GAs) for system optimization. …”
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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. …”
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Pig Detection Algorithm Based on Sliding Windows and PCA Convolution by Longqing Sun, Yan Liu, Shuaihua Chen, Bing Luo, Yiyang Li, Chunhong Liu

“…The principal component analysis convolution kernels were trained to extract foreground and background features of pig images. The support vector machine was used to classify sliding windows to obtain windows where pigs were located, and the non-maximum suppression algorithm was used to eliminate redundant windows to complete the target detection. …”
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Firefly Algorithm based Feature Selection for Arabic Text Classification by Souad Larabi Marie-Sainte, Nada Alalyani

“…To validate this technique, Support Vector Machine classifier is used as well as three evaluation measures including precision, recall and F-measure. …”
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