Monitoring and Assessment of Slope Hazards Susceptibility Around Sarez Lake in the Pamir by Integrating Small Baseline Subset InSAR with an Improved SVM Algorithm by Yang Yu, Changming Zhu, Majid Gulayozov, Junli Li, Bingqian Chen, Qian Shen, Hao Zhou, Wen Xiao, Jafar Niyazov, Aminjon Gulakhmadov

“…These deformation measurements were combined with key environmental factors to construct a susceptibility evaluation model based on the Information Value and Support Vector Machine (IV-SVM) methods. The results revealed a distinct spatial deformation pattern, characterized by greater activity in the western region than in the east. …”
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The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes by Chaoling Wu, Wanyi Liu, Pengfei Mei, Yunyun Liu, Jian Cai, Lu Liu, Juan Wang, Xuefeng Ling, Mingxue Wang, Yuanyuan Cheng, Manbi He, Qin He, Qi He, Xiaoliang Yuan, Jianlin Tong

“…While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. …”
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Older people and stroke: a machine learning approach to personalize the rehabilitation of gait by Elvira Maranesi, Federico Barbarossa, Leonardo Biscetti, Marco Benadduci, Elisa Casoni, Ilaria Barboni, Fabrizia Lattanzio, Lorenzo Fantechi, Daniela Fornarelli, Enrico Paci, Sara Mecozzi, Manuela Sallei, Mirko Giannoni, Giuseppe Pelliccioni, Giovanni Renato Riccardi, Valentina Di Donna, Roberta Bevilacqua

“…This underscores the basal ganglia’s role in motor control, sensory processing, and postural control, highlighting the importance of sensory input in post-stroke rehabilitation.…”
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Regression-based leaf nitrogen concentration estimation of young Cephalotaxus hainanensis in small and imbalanced samples by Mengmeng Shi, Tian Wang, Ling Lin, Qingxue Li, Zhulin Chen, Xingjing Chen, Peng Wang, Xuefeng Wang

“…The study also utilized advanced metrics (F1-score, recall, and precision for regression) for evaluating regression models to compare and assess the accuracy of support vector regression (SVR) and gradient-boosted trees (XGBoost) combined with different pretreatments, particularly emphasizing prediction accuracy in rare cases. …”
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Reinforcement learning energy management control strategy of electric tractor based on condition identification by Liqiao Li, Jiangchun Chen, Jing Nie, Zongyu Gao

“…Firstly, the power demand of ET during driving is regarded as a Markov process. The historical driving data are used to construct the driving conditions of ET and obtain the Markov power state transfer probability matrix(MPSTPM) under different CI; Second, to minimize the energy consumption of lithium-titanate battery and supercapacitor hybrid power system(HPS), the power allocation strategy for ET under different CI is obtained by a Q-network RL algorithm; Finally, an learning vector quantization neural network(LVQNN) is used to identify the current ET driving CI through online and real-time, and the control system makes real-time power output decision through the current driving CI. …”
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In situ airborne measurements of atmospheric parameters and airborne sea surface properties related to offshore wind parks in the German Bight during the project X-Wakes by A. Lampert, R. Hankers, T. Feuerle, T. Rausch, M. Cremer, M. Angermann, M. Bitter, J. Füllgraf, H. Schulz, U. Bestmann, K. B. Bärfuss

“…The instrumentation of both aircraft consisted of a nose boom with sensors for measuring the wind vector, temperature and humidity and, additionally, a surface temperature sensor. …”
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MODERN DIAGNOSTICS METHODS OF NON-SPECIFIC PROTECTION OF PERIODONTAL TISSUES IN RESIDENTS OF THE INDUSTRIAL REGION by A.V. Samoilenko, S.V. Pavlov, I.V. Vozna

“…Statistica 13.0 licensed number JPZ804I382130ARCN10-J was used to process the results. The results of the study and their discussion. …”
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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…Second, the training and prediction effects of three machine learning prediction models—support vector machine, BP neural network, and random forest—were compared, and the BP neural network was selected as the machine learning mathematical model for injection allocation optimization. …”
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Harnessing Multiscale Topographic Environmental Variables for Regional Coral Species Distribution Models by Annie S. Guillaume, Renata Ferrari, Oliver Selmoni, Véronique J. L. Mocellin, Hugo Denis, Melissa Naugle, Emily Howells, Line K. Bay, Stéphane Joost

“…The ACA and DeepReef DEMs shared similar vertical accuracies, each producing topographic variables relevant to marine SDMs. Slope and vector ruggedness measure (VRM), capturing hydrodynamic movement and shelter or exposure, were the most relevant variables in SDMs of all three species. …”
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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

“…Support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and four stacked models were developed. …”
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Surface-based morphometry of the cerebral cortex in cognitive impairments of varying severity in patients with age-related cerebral small vessel disease by Elena I. Kremneva, Larisa A. Dobrynina, Kamila V. Shamtieva, Victoria V. Trubitsyna, Zukhra S. Gadzhieva, Angelina G. Makarova, Maria M. Tsypushtanova, Marina V. Krotenkova

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Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters by Jingru Huang, Yong Li, Xiaotan Pan, Jixiu Wei, Qiongqian Xu, Yin Zheng, Peng Chen, Jiabo Chen

“…Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. …”
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An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders by Xuening Lyu, Rimsa Goperma, Dandan Wang, Chunling Wan, Liang Zhao

“…Subsequently, a Support Vector Machine (SVM) method is employed for psychiatric disorder classification utilizing feature vectors extracted from the segmentation of NSR areas with a 3-scale quantization. …”
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TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines by Sadam Hussain, Usman Naseem, Mansoor Ali, Daly Betzabeth Avendaño Avalos, Servando Cardona-Huerta, Beatriz Alejandra Bosques Palomo, Jose Gerardo Tamez-Peña

“…Abstract Background Recently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. …”
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Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types by Mahrukh Zakir, Alishbah Saddiqa, Mawara Sheikh, Lalarukh Zakir, Fatima Sami, Faisal Sardar Ahmad, Sadaf Abdul Rauf, Iqra Ali, Zahid Muneer, Wadi B. Alonazi, Abdul Rauf Siddiqi

“…RNA Seq datasets consisting of 49 HER2 + and 44 TNBC breast tumor samples were retrieved and pre-processed. Differentially Expressed Genes (DEGs) along with their logFC and p-values were fetched. …”
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Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model by Tingting Hu, Liheng Zhao, Xueling Zhao, Lin He, Xiaoli Zhong, Zhe Yin, Junjie Chen, Yanting Han, Ka Li

“…Six machine learning models, namely Logistic regression(LR), Support Vector Machine(SVM), K-Nearest Neighb(KNN), Random Forest (RF), Light Gradient Boosting Machine(LightGBM), and eXtreme Gradient Boosting‌(XGBoost) were constructed on this basis. …”
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Multi‐sequence MRI‐based clinical‐radiomics models for the preoperative prediction of microsatellite instability‐high status in endometrial cancer by Zhuang Li, Yi Su, Yongbin Cui, Yong Yin, Zhenjiang Li

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Epidemiological Characteristics of Tick-Borne Encephalitis in the Sverdlovsk District over a 20-Year Period by N. M. Kolyasnikova, L. G. Chistyakova, A. V. Ponomareva, A. E. Platonov, V. V. Romanenko, А. A. Ishmukhametov, V. G. Akimkin

“…To analyze the main indicators of the manifestation of the epidemic process of TBE in the territory of the Sverdlovsk district under the conditions of planned vaccination over a 20-year period (2002–2021).Materials and methods. …”
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The evaluation of the hemodynamically significant stenosis of the carotid arteries: analyzing the results from the duplex scanning of vessels, from the computed tomographic and the... by Nataly S. Nosenko, Ekaterina M. Nosenko, Daria S. Alemasova, Tatiana V. Dedy

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TATPat based explainable EEG model for neonatal seizure detection by Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva

“…Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). …”
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