Copula-Driven Learning Techniques for Physical Layer Authentication Using Multimodal Data by Sahana Srikanth, Sanjeev Gurugopinath, Sami Muhaidat

“…For the classification task, we consider some of the well-known learning algorithms including long short-term memory (LSTM), random forest, K-nearest neighbor, support vector machine and bagging tree techniques. Moreover, we study the effect of correlation introduced across the attributes, and compare it with the case with no correlation among the attributes. …”
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Bamboo Growing and Poverty Reduction in Musanze and Burera Districts, Rwanda. by Eric, Hitimana

“…It is therefore recommended that bamboo growing in Musanze and Burera district should be given priority compared to other tree varieties in the district planning and budgeting processes. …”
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Odgovornost za opasnost od zakašnjenja u prijevozu robe morem by Vesna Polić Foglar

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Forecasting Dissolved Organic Carbon Levels Utilizing Metaheuristic Optimization with Artificial Intelligence Techniques by Peng He, Chengjun Ma

“…Specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Decision Tree (DT), and Support Vector Regression (SVR) algorithms were employed. …”
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Estimating the Torsional Capacity of Reinforced Concrete Beams Using ANFIS Models by Zhao Wenwu, Zeng Shaowu, Gong Guilin, Li Qiangqiang, Li Kexing

“…This work evaluates and determines the most effective tree-based machine learning algorithms to estimate the torsional capacity (𝑇𝑟) of 𝑅𝐶 beams subjected to pure torsion. …”
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Triple (and quadruple) soft-gluon radiation in QCD hard scattering by Stefano Catani, Dimitri Colferai, Alessandro Torrini

“…In the soft limit the corresponding scattering amplitude has a singular behaviour that is factorized and controlled by a colorful soft current. We compute the tree-level current for triple soft-gluon emission from both massless and massive hard partons. …”
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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

“…Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. All tested models successfully correlate and model the solubility data within an acceptable accuracy. …”
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Deteksi Cyberbullying dengan Mesin Pembelajaran Klasifikasi (Supervised Learning): Peluang dan Tantangan by Yudi Setiawan, Nur Ulfa Maulidevi, Kridanto Surendro

“…Implementasi Machine Learning untuk deteksi cyberbullying dapat dilakukan dengan berbagai algoritma, seperti algoritma probabilistik (Naïve Bayes) maupun supervised learning (Support Vector Machine, k-Nearest Neighbour, Decission Tree), dan metode lainnya yang hingga saat ini terus dikembangkan dengan berbagai pendekatan untuk meningkatkan akurasi deteksi cyberbullying atau non-cyberbullying. …”
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Analgesic and Anti-inflammatory Activities of the Stem Bark of Yellow Flamboyant (<i>Peltophorum pterocarpum</i>) by O.K. Ogbeide, C.J. Ogbonnaya, E. Asakitikpi, D.O. Uyi, B.O. Aluge, O. Nosakhare, W.O. Otahagwa, C.A. Ehigie, U. Omoruyi, O.S. Aifuwa, B.O. Gabriel

“…Different parts of this tree are being used for the management of many diseases. …”
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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. …”
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Coexistence of spiders in floodplain forests as an indicator of ecological stability and landscape sustainability in the inland Danube Delta by Krumpálová Zuzana, Šustek Zbyšek

“…Environmental conditions, i.e. groundwater level, flood regime, vegetation and tree shading, were defined as the main factors. The presence or absence of flooding and the depth of the water table had a significant effect on spider community structure. …”
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The role of natural products targeting macrophage polarization in sepsis-induced lung injury by Yake Li, Sinan Ai, Yuan Li, Wangyu Ye, Rui Li, Xiaolong Xu, Qingquan Liu

“…It highlights how NPs mitigate macrophage imbalances to alleviate SALI, focusing on key signaling pathways such as PI3K/AKT, TLR4/NF-κB, JAK/STAT, IRF, HIF, NRF2, HMGB1, TREM2, PKM2, and exosome-mediated signaling. NPs influencing macrophage polarization are classified into five groups: terpenoids, polyphenols, alkaloids, flavonoids, and others. …”
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Citrus Irrigation Management by Davie Mayeso Kadyampakeni, Kelly T. Morgan, Mongi Zekri, Rhuanito Ferrarezi, Arnold Schumann, Thomas A. Obreza

“…Any degree of water stress or imbalance can produce a deleterious change in physiological activity of growth and production of citrus trees.  The number of fruit, fruit size, and tree canopy are reduced and premature fruit drop is increased with water stress.  …”
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Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania by Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar

“…A variety of supervised machine-learning algorithms such as RBF Kernel in SVM, Linear Kernel in SVM, Polynomial Kernel in SVM, Random Forest, Logistic regression classifier, Decision tree, Gradient Boosting, K-Nearest Neighbours Classifier, Na&#x00EF;ve Bayes Classifier, Artificial Neuron Network and Ensemble Learning Model were implemented to predict multidimensional poverty status for each dataset. …”
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In silico analysis of human herpes virus-8 genome: a comparison of the K1, VR1, and VR2 regions for genotyping and global geographical distribution by Nastaran Khodadad, Ava Hashempour, Shokufeh Akbarinia

“…Afterwards, the VR1 and VR2 regions were derived from the K1 genes, and genotyping of the K1, VR1, and VR2 sequences was performed by applying phylogenetic tree and BioAfrica methods. The K1 genotyping result was most similar to that of VR1, followed by VR2. …”
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Gravitational waves and dark matter in the gauged two-Higgs doublet model by Michael J. Ramsey-Musolf, Van Que Tran, Tzu-Chiang Yuan

“…The G2HDM introduces a dark replica of the Standard Model electroweak gauge group, inducing an accidental Z 2 symmetry which not only leads to a simple scalar potential at tree-level but also offers a compelling vectorial dark matter candidate. …”
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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms by Mana Alyami, Irfan Ullah, Furqan Ahmad, Hisham Alabduljabbar

“…Conventional ML techniques like random forest (RF) and decision tree (DT) were also employed for comparison. A dataset of 346 experimental data sets from existing literature was used to evaluate model performance. …”
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Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations by Bartosz Czernecki, Michał Marosz, Joanna Jędruszkiewicz

“…We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. …”
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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection by Lu Liang, Jacob Daniels

“…This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine. …”
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From boundary data to bound states by Gregor Kälin, Rafael A. Porto

“…We also provide closed-form expressions for the orbital frequency and periastron advance at tree-level and one-loop order, respectively, which capture a series of exact terms in the Post-Newtonian expansion. …”
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