Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER)... by Xu Zheng, Longqiang Shu, Shanyi Lin, Hanqiang Jin, Xiaoyu Wang, Ting Yuan

“…Ensemble learning and survival support vector machine with different kernel methods were developed and compared for their prognostic performance. …”
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Identification of Food/Nonfood Visual Stimuli from Event-Related Brain Potentials by Selen Güney, Sema Arslan, Adil Deniz Duru, Dilek Göksel Duru

“…We have implemented k-nearest neighbor (kNN), support vector machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Bayesian classifier, decision tree (DT), and Multilayer Perceptron (MLP) classifiers on these datasets. …”
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Lithological mapping and spectroscopic studies of carbonatite and clinopyroxenite from Hogenakkal carbonatite complex, India by Saraah Imran, Sourav Bhattacharjee, Ajanta Goswami, Aniket Chakrabarty

“…Petrography, Raman spectroscopy of minerals, and spectroradiometric measurements of rock samples support the interpretations derived from Principal Component Analysis (PCA), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), Decision Tree, and Random Forest algorithms, thereby aiding in the identification of lithological variations and potential clinopyroxenite occurrences. …”
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Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy by Daisuke Nagasato, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Tomoaki Sonobe, Masahiro Kameoka, Masanori Niki, Ken Hayashi, Yoshinori Mitamura

“…The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. …”
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Satellite imagery, big data, IoT and deep learning techniques for wheat yield prediction in Morocco by Abdelouafi Boukhris, Antari Jilali, Abderrahmane Sadiq

“…All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. …”
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An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture by MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan

“…The Stacking ensemble technique, with Support Vector Classifier (SVC) as the meta-classifier, achieved the highest overall accuracy of 95.9%. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. …”
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Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment f... by Sujin Kim, Ah Young Kim, Yu-Bin Shin, Seonmin Kim, Min-Sup Shin, Jinhwa Choi, Kyung Lyun Lee, Jisu Lee, Sangwon Byun, Heon-Jeong Lee, Chul-Hyun Cho

“…We will employ various machine-learning techniques, including random forest, support vector machine and deep-learning models. …”
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Linear and Nonlinear Multivariate Classification of Iranian Bottled Mineral Waters According to Their Elemental Content Determined by ICP-OES by J.B. Ghasemi, E. Zolfonoun, R. Khosrokhavar

“…The combinations of inductively coupled plasma-optical emission spectrometry (ICP-OES) and three classification algorithms, i.e., partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM) and soft independent modeling of class analogies (SIMCA), for discriminating different brands of Iranian bottled mineral waters, were explored. …”
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New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale by Qiong Zheng, Yihao Chen, Qing Xia, Yunfei Zhang, Dan Li, Hao Jiang, Chongyang Wang, Longlong Zhao, Wenjiang Huang, Yingying Dong, Chuntao Wang

“…GRVI_RB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). …”
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Integrative genomic analysis and diagnostic modeling of osteoporosis: unraveling the interplay of autophagy, osteogenesis, adipogenesis, and immune infiltration by Lin-Jing Han, Jian-Zong Zhu, Jian-Zong Zhu, Hong-Cai Liu, Xiao-Sheng Lin, Xiao-Sheng Lin, Shu-Zhong Yang

“…The diagnostic model, developed utilizing logistic regression, support vector machine (SVM), and the least absolute shrinkage and selection operator (LASSO), pinpointed nine pivotal genes—AKT1, NFKB1, TNF, CTNNB1, LMNA, BHLHE40, BMP4, WNT1, and COPS3—and confirmed their diagnostic efficacy through validation. …”
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An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer by Xue-Feng Gao, Can-Gui Zhang, Kun Huang, Xiao-Lin Zhao, Ying-Qiao Liu, Zi-Kai Wang, Rong-Rong Ren, Geng-Hui Mai, Ke-Ren Yang, Ye Chen

“…The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.Results GC patients with <3 years of survival showed a higher abundance of Aggregatibacter and diminished abundances of Filifactor and Moryella than those who survived ≥3 years. …”
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Identification and validation of endoplasmic reticulum autophagy-related potential biomarkers in periodontitis by Ruyue Wang, Jinyue Hu, Qing Sun, Shuixiang Guo, Gege Zhang, Ao Lu, Shuo Liu, Xue Yang, Lina Wang

“…Random forest, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature removal (SVM-RFE) algorithms were used to identify hub genes. …”
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Adaptive zero velocity correction method for fiber optic inertial navigation system in coal mining roadheader by Qinghua MAO, Qing ZHOU, Jianquan CHAI, Yanzhang CHEN, Wenjuan YANG, Xusheng XUE

“…Therefore, an adaptive zero-speed correction method for fiber-optic inertial navigation of coal mine roadheader based on zero-speed detection and extended Kalman filter is proposed.Aiming at the problem of inaccurate zero-speed detection of traditional threshold method for roadheader fiber-optic inertial navigation, a zero-speed detection method based on PCA−SCSO−SVM ( Principal Component Analysis PCA, Sand Cat Swarm Optimization SCSO, Support Vector Machine SVM ) is proposed. This method uses roadheader vibration signal for zero-speed detection. …”
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Tanshinone Content Prediction and Geographical Origin Classification of <i>Salvia miltiorrhiza</i> by Combining Hyperspectral Imaging with Chemometrics by Yaoyao Dai, Binbin Yan, Feng Xiong, Ruibin Bai, Siman Wang, Lanping Guo, Jian Yang

“…Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were employed to discriminate 420 <i>Salvia miltiorrhiza</i> samples collected from Shandong, Hebei, Shanxi, Sichuan, and Anhui Provinces. …”
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Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…These reconstructed features serve as input to a classification module, comprising a multi-layer neural network, GCN, adept at processing graph-structured data, alongside conventional machine learning classifiers such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), facilitating categorization of chest X-ray images into COVID-19, pneumonia, and normal cases. …”
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Inversion model of stress state reconstruction for geological hazard pipelines based on digital twin by Xue Luning, Tian Mingliang, Zhao Juncheng

“…The mechanical state of the physical pipeline is mapped in real time by the digital twin, the numerical simulation and multi-source monitoring data are integrated, and the parameters of the twin model are dynamically optimized by combining the optimization algorithms of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM), so as to realize the real-time prediction of the pipeline stress state and the dynamic updating of the disaster scenario. …”
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Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography. by Mahesh B Nagarajan, Paola Coan, Markus B Huber, Paul C Diemoz, Axel Wismüller

“…The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). …”
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Intelligent UAV health monitoring: Detecting propeller and structural faults with MEMS-based vibration by Temel Sonmezocak

“…For fault detection, the performance of Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Neural Network (NN) algorithms was evaluated. …”
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