Detection of offensive content in the Kazakh language using machine learning and deep learning approaches by Milana Bolatbek, Moldir Sagynay, Shynar Mussiraliyeva, Zhastay Yeltay

“…Given the agglutinative structure and rich morphology of Kazakh, standard natural language processing (NLP) models require significant adaptation. …”
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TradeWise: Towards Context-Aware Stock Market Predictions with Sentiment and Political Insights by Andreas Marpaung, David Masterson

“…This enriched dataset is employed to train four different machine learning algorithms: a Hybrid, a Random Forest Model (RFM), a Support Vector Machine (SVM), and a K-Nearest Neighbors (KNN) model. …”
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State of communication regimes in the area of the Greater Caspian Sea by V. G. Golovin, E. E. Golovina

“…This format is determined by two interrelated processes: the state of the geopolitical landscape, as well as the friendliness of communication regimes. …”
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Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map by Payam Piray, Nathaniel D. Daw

“…Here, we propose a novel computational model for efficiently creating and planning with compositional predictive maps, which successfully reproduces response fields in the medial entorhinal cortex, particularly object vector cells and grid cells. The model treats each object as an alteration to a baseline map linked to open space, creating predictive maps by combining object-related representations compositionally, providing insights into brain processes supporting efficient, flexible planning.…”
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The use of cytokines in the treatment of cornea inflammatory and dystrophic diseases (review) by M. S. Denisko, O. I. Krivosheina

“…In this connection, a new vector of search for pathogenetically directed ways to improve and restore lost functions using both individual representatives of cytokines and their natural complex is determined. …”
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Identification of Formaldehyde under Different Interfering Gas Conditions with Nanostructured Semiconductor Gas Sensors by Lin Zhao, Jing Wang, Xiaogan Li

“…Sensor array with pattern recognition method is often used for gas detection and classification. Processing time and accuracy have become matters of widespread concern in using data analysis with semiconductor gas sensor array for volatile organic compound gas mixture classification. …”
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Effects of urban sprawl on land use change in the peripheral villages of Tehran metropolis (case study: Tehran-Damavand axis) by ِAshkan Mohammadi, Naser Shafiei Sabet, Alireza Shakiba

“…After field operation and harvesting of samples with two-frequency GPS receivers and introducing it to the software, the classification of complications was performed by support vector machines with a mean total accuracy of 62.69% and a mean Kappa coefficient of 85.33%. …”
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Optimized Machine Learning-Augmented Hybrid Empirical Models for AlGaN/GaN HEMTs: A Comprehensive Analysis by Ahmad Khusro, Saddam Husain, Mohammad Hashmi

“…Thereafter, six extensively optimized ML regression models, namely decision tree (DT), ensemble learning (EL), support vector regression (SVR), kernel approximation regression (KAR), Gaussian process regression (GPR), and neural networks (NN) are employed to simulate the intrinsic behavior of GaN HEMTs. …”
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AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction by Mohamed Sahraoui, Aissa Laouissi, Yacine Karmi, Abderazek Hammoudi, Mostefa Hani, Yazid Chetbani, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout

“…Six predictive models were assessed for accuracy and generalization: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Model (LM), Dragonfly Algorithm-based Deep Neural Network (DNN-DA), and Improved Grey Wolf Optimizer-based Deep Neural Network (DNN-IGWO). …”
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Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging by Quancheng Liu, Chunzhan Yu, Yuxuan Ma, Hongwei Zhang, Lei Yan, Shuxiang Fan

“…Subsequently, a nonlinear support vector regression (SVR) model was used to perform regression modeling for the six quality parameters. …”
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Equivalent method for DFIG wind farms based on modified LightGBM considering voltage deep drop faults by Xuecheng Liu, Peixiao Fan, Jun Yang, Song Ke, Binyu Ma, Yangzhou Pei, Jian Xu

“…The factors influencing the Crowbar action state and trip-off state of wind turbines are identified, and a feature vector for wind turbine operating states is constructed. …”
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Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning... by Feiyang Xia, Tingting Fan, Mengjie Wang, Lu Yang, Da Ding, Jing Wei, Yan Zhou, Dengdeng Jiang, Shaopo Deng

“…The accuracy and Area Under the Curve (AUC) achieved by Support Vector Machines (SVM) were impressive, with values of 0.87 and 0.99, respectively. …”
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STATIC OF ORTHODONTIC APPLIANCES WITH MOVABLE INCLINED PLANE FOR MESIAL BATE TREATMENT by P.S. Flis, A.I. Hryhorenko, N.M. Doroshenko, M.M. Tormakhov, V.V. Filonenko

“…Since the orthodontic force vector is in vestibular direction, the angle β is in the range from -30° to α. …”
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Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat by Zhikai Cheng, Xiaobo Gu, Yadan Du, Zhihui Zhou, Wenlong Li, Xiaobo Zheng, Wenjing Cai, Tian Chang

“…Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks (ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out. …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

“…A diverse set of machine learning models, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, Gaussian Naive Bayes, and Multilayer Perceptron (MLP), were evaluated alongside ensemble methods to achieve robust and reliable predictions. …”
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Identification of cellular signatures associated with chinese hamster ovary cell adaptation for secretion of antibodies by Ying Bai, Ivan Domenech Mercadé, Ramy Elgendy, Giulia Lambiase, Sew Peak-Chew, Catarina Franco, Steven W. Wingett, Tim J. Stevens, Luigi Grassi, Noah Hitchcock, Cristina Sayago Ferreira, Diane Hatton, Elizabeth A. Miller, Rajesh K. Mistry

“…Unconventional biological drugs with complex structures and processing requirements are particularly problematic. …”
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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

“…Four basic ML algorithms including support vector machine (SVM), K-nearest neighbor (kNN), decision tree (DT), and naive Bayes (NB), and five deep learning models including convolutional neural network (CNN), long-short term memory (LSTM), bidirectional long short-term memory (BiLSTM), and CNN-BiLSTM were used to process the EMG signals recorded under different gaits. …”
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机器学习算法在食用植物油掺伪鉴别中应用的 研究进展Research progress on application of machine learning algorithms in adulteration identification of edible vegetable oils... by 吕壮，黄金，兰梓溶，代婷玉，许宙，陈茂龙，焦叶，文李，程云辉，丁利 LYU Zhuang, HUANG Jin, LAN Zirong, DAI Tingyu, XU Zhou, CHEN Maolong, JIAO Ye, WEN Li, CHENG Yunhui, DING Li

“…In order to provide a theoretical basis for the selection of algorithms in the adulteration identification of edible vegetable oils, the classification of machine learning algorithms and their application process in the adulteration identification of edible vegetable oils were briefly introduced. …”
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Breaking Digital Health Barriers Through a Large Language Model–Based Tool for Automated Observational Medical Outcomes Partnership Mapping: Development and Validation Study by Meredith CB Adams, Matthew L Perkins, Cody Hudson, Vithal Madhira, Oguz Akbilgic, Da Ma, Robert W Hurley, Umit Topaloglu

“…MethodsWe developed a 3-tiered semantic matching system using GPT-3 embeddings to transform heterogeneous clinical data to the OMOP Common Data Model. The system processes input terms by generating vector embeddings, computing cosine similarity against precomputed Observational Health Data Sciences and Informatics vocabulary embeddings, and ranking potential matches. …”
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Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation by Xin Chang Zou, Xue Peng Rao, Jian Biao Huang, Jie Zhou, Hai Chao Chao, Tao Zeng

“…After a rigorous screening process, a total of 4,108 patients were selected for further analysis, divided in a 7:3 ratio into a training cohort and an internal validation cohort. …”
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