Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques by Zheshu Jia, Deyun Chen

“…In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. …”
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Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks by Jiacheng Dong, Yuan Chen, Gang Guan

“…Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. …”
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Identification of Respiration Types Through Respiratory Signal Derived From Clinical and Wearable Electrocardiograms by Agnese Sbrollini, Micaela Morettini, Ennio Gambi, Laura Burattini

“…Respiration classification was performed through a linear support vector machine and evaluated by F1 score. …”
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Detection of Adulteration of Panax Notoginseng Powder by Terahertz Technology by Bin Li, Hai Yin, A-kun Yang, Ai-guo Ouyang

“…Then, the least square support vector machine (LS-SVM) algorithm and partial least square (PLS) algorithm are used to establish the quantitative analysis model. …”
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A correlation-based binary particle swarm optimization method for feature selection in human activity recognition by Huaijun Wang, Ruomeng Ke, Junhuai Li, Yang An, Kan Wang, Lei Yu

“…Experimental results show that the proposed method can work well with six classifiers, namely, J48, random forest, k -nearest neighbor, multilayer perceptron, naïve Bayesian, and support vector machine, and the new algorithm can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.…”
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An assessment of the long-term change of the Mersin west coastline using digital shoreline analysis system and detection of pattern similarity using fuzzy C-means clustering by Ozcan Zorlu, Lutfiye Kusak

“…The Google Earth Engine (GEE) platform facilitated data acquisition, classification, and edge detection. A Support Vector Machine (SVM) classification algorithm was applied to distinguish land from water. …”
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A novel infrared thermography image analysis for transformer condition monitoring by Rupali Balabantaraya, Ashwin Kumar Sahoo, Prabodh Kumar Sahoo, Chayan Mondal Abir, Manoj Kumar Panda

“…Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). …”
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Providing a Framework for Assessing and Evaluating Network Data Studies in the Fight Against Social Anomalies by Mohammad Khalili, Hamzehali Nourmohammadi, Nader Naghshineh

“…Additionally, clustering techniques, such as k-means, were employed to identify different forms of theft crimes. Classification algorithms, including neural networks, Bayesian rules, Bayesian navigation, and support vector machines, were used to predict theft crimes. …”
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Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification by Vaibhavi Patel, Hetal Bhavsar

“…In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. …”
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Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model by Akshat Gaurav, Brij B. Gupta, Razaz Waheeb Attar, Ahmed Alhomoud, Varsha Arya, Kwok Tai Chui

“…The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. …”
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Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM by Qin Bo, Sun Guodong, Zhang Liqiang, Liu Yongliang, Zhang Chao, Wang Jianguo

“…For the problem that the characterization of the gear fault signal feature is difficult to extract and the structure parameters selection of support vector machine（ SVM） are based on experience leads the poor precision and generalization ability of fault state recognition,a method that IPSO- SVM rolling bearing fault diagnosis based on the Hilbert envelope spectrum singular value is proposed. …”
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A Novel Hybrid Method for Short-Term Power Load Forecasting by Huang Yuansheng, Huang Shenhai, Song Jiayin

“…Thirdly, least square support vector machine (LSSVM) and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH) are employed to forecast the subseries, respectively, based on the characteristics of power load series. …”
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Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. by Maxim Topaz, Maryam Zolnoori, Allison A Norful, Alexis Perrier, Zoran Kostic, Maureen George

“…Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study's predictive goals.…”
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An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes by Juan Lu, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, Bing Huang

“…Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. …”
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Improved UWB-based indoor positioning system via NLOS classification and error mitigation by Shoude Wang, Nur Syazreen Ahmad

“…To address this issue, we propose a method for identifying and classifying NLOS signals based on Support Vector Machine Recursive Feature Elimination (SVM-RFE). …”
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A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure by Yan Xue, Weiliang Gao, Yanming Zhao

“…Moreover, a concrete durability prediction model based on particle swarm optimization-least squares support vector machine (PSO-LSSVM) and improved NSGA-II (nondominated sorting genetic algorithm II) algorithm was proposed to quickly and accurately determine the optimization scheme of self-healing concrete mix proportion. …”
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Predictive modeling for rework detection in sustainable building projects by AbdulLateef Olanrewaju, Kafayat Shobowale

“…Feature scaling and normalisation were performed across the dataset to standardise the features. Six machine learning models that comprised support vector machine, Adaboost, Logistic regression, a K-nearest neighbour, neural network and random forest classifier were trained to predict the occurrence of reworks in sustainable buildings. …”
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Unleashing the power of intelligence: revolutionizing malaria outbreak preparedness with an advanced warning system in Benin, West Africa by Gouvidé Jean Gbaguidi, Nikita Topanou, Walter Leal Filho, Komi Agboka, Guillaume K. Ketoh

“…Subsequently, an intelligent model for forecasting malaria outbreaks was developed using support vector machine (SVM) algorithm. The developed model for malaria outbreaks was then employed to establish an intelligent system for warning and forecasting malaria incidence on a monthly basis, utilising the Meteostat platform, an online weather data service provider, in conjunction with the Streamlit framework. …”
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Automated Assessment of Student Self-explanation During Source Code Comprehension by Jeevan Chapagain, Lasang Tamang, Rabin Banjade, Priti Oli, Vasile Rus

“…We explored a number of models consisting of textual features in conjunction with machine learning algorithms such as Support Vector Regression (SVR), Decision Trees (DT), and Random Forests (RF). …”
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Development and Practice of Cloud Collaborative Platform for Downhole Measurement Tools by Che Yang, Yuan Guangjie, Qian Hongyu, Du Weiqiang, Wang Chenlong, Ding Jiping

“…Moreover， to solve the problem of low far-field ranging accuracy， multiple magnetic steering data mining algorithms such as support vector machine （SVM）， decision tree （DT）， multilayer perceptron （MLP） and convolutional neural network （CNN） were built and compared， indicating that the robustness and generalization of the multilayer perceptron algorithm is the best. …”
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