Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning by Ahmad Aldelemy, Raed A. Abd-Alhameed

“…Our methodology includes data analysis, transformation, training, and testing machine learning classifiers such as Naïve Bayes, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Artificial Neural Networks, AdaBoost, and Gradient Descent. …”
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Higgs Production at µ+µ+ Colliders. Based on hep-ph/2408.01068 [1] by Treuer Lukas

“…Thus, the higher-order cross section with polarized beams can become about half as large as the leading-order process at µ+µ− colliders at O(10) TeV energies, despite its naive suppression. When calculating the higher-order cross section, collinear photon emissions make direct computations using event generators difficult. …”
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COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms by Nasiba M. Abdulkareem, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dathar A. Hasan

“…Weka is used to run tests on real-world data, and four output classification algorithms (Decision Tree, K-nearest neighbors, Random Tree, and Naive Bayes) are used to analyze and draw conclusions. …”
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Adverse Immunologically Mediated Oral Mucosal Reactions to Systemic Medication: Lichenoid Tissue Reaction/Interface Dermatitis-Stomatitis, Autoimmune Vesiculobullous Disease, and I... by R. A. G. Khammissa, R. Chandran, A. Masilana, J. Lemmer, L. Feller

“…The type of hypersensitivity immune response generated, whether antibody mediated or T cell mediated, or an immune complex reaction is determined by multiple factors, including the molecular characteristics of the allergen, the route of administration of the medication, the manner of presentation of the allergen by antigen-presenting cells to naïve T cells, the repertoire of the T cell receptors, and the cytokine profile within the microenvironment. …”
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A replication-incompetent adenoviral vector encoding for HSV-2 gD2 is immunogenic and protective against HSV-2 intravaginal challenge in mice. by Elisa Rossetti, Marija Vujadinovic, Ella van Huizen, Jeroen Tolboom, Hanneke Schuitemaker, Feng Yao, Roland Zahn, Eirikur Saeland

“…Transfer of immune serum into naïve BALB/c mice before intravaginal challenge confirmed the role of antibodies in the protection of mice against infection although other immune factors may play a role as well.…”
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Enigma of Retrotransposon Biology in Mammalian Early Embryos and Embryonic Stem Cells by Ying Yin, Liquan Zhou, Shuiqiao Yuan

“…Notably, abundant retrotransposon expression in mouse embryonic stem cells (ESCs) marks transient totipotency status, while retrotransposon enrichment in human ESCs indicates naive-like status. Some retrotransposon elements retained the capacity to retrotranspose, such as LINE1, producing genetic diversity or disease. …”
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Impact of Pentoxifylline and Vitamin E on Ribavirin-Induced Haemolytic Anaemia in Chronic Hepatitis C Patients: An Egyptian Survey by M. Assem, M. Yousri

“…Patients and Methods. Selected 200 naïve chronic HCV patients, were randomized to receive either the standard antiviral therapy (peginterferon α-2b and RBV) plus pentoxifylline (800 mg) and high-dose vitamin E (1000 iu) daily (combined group) or received standard antiviral therapy plus placebo only (control group). …”
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Evaluation of Different Stemming Techniques on Arabic Customer Reviews by Hawraa Fadhil Khelil, Mohammed Fadhil Ibrahim, Hafsa Ataallah Hussein, Raed Kamil Naser

“…This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). …”
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Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories by Abdullah Al-Hashedi, Belal Al-Fuhaidi, Abdulqader M. Mohsen, Yousef Ali, Hasan Ali Gamal Al-Kaf, Wedad Al-Sorori, Naseebah Maqtary

“…Two pretrained continuous bag-of-words (CBOW) models are investigated, and Naïve Bayes was used as a baseline classifier. Several single-based and ensemble-based machine learning classifiers have been used with and without SMOTE (synthetic minority oversampling technique). …”
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FSAS: An IoT-Based Security System for Crop Field Storage by Chandra Prakash, Anurag Barthwal, Shwetank Avikal, Gyanendra Kumar Singh

“…The sensor signal is transmitted to the cloud whenever the intruder is within the sensing range of the sensor node. Naive Bayes’ prediction model is used to identify the level of encroachment as no (green), mild (yellow), or high (red) threat. …”
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Improving Model Performance for Predicting Exfiltration Attacks Through Resampling Strategies by Arif Rahman Hakim, Kalamullah Ramli, Muhammad Salman, Esti Rahmawati Agustina

“…Furthermore, we evaluate model performance across five classifiers: Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, and XGBoost. …”
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Targeted localization of the mother centrosome in CD8+ T cells undergoing asymmetric cell division promotes memory formation by Niculò Barandun, Benjamin Meier, Gautier Stehli, Fabienne Gräbnitz, Nathan Zangger, Annette Oxenius

“…Summary: How a single, naive T cell can give rise to diverse progenies of effector and memory cells is not completely understood. …”
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Reinforced colour preference of parasitoid wasps in the presence of floral scent: a case study of a cross-modal effect by Soichi Kugimiya, Takeshi Shimoda, Junji Takabayashi

“…Abstract We examined the possibility of a cross-modal effect in naïve Cotesia vestalis, a parasitoid wasp of diamondback moth larvae, by using artificial flower models of four colours (blue, green, yellow, and red) in the absence or presence of floral scent collected from Brassica rapa inflorescences. …”
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Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics by Vladimir S. Kublanov, Anton Yu. Dolganov, David Belo, Hugo Gamboa

“…The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. …”
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Why and How to Treat Chronic Hepatitis C by Stephanos J Hadziyannis

“…IFN-α monotherapy for one year can induce sustained response (SR) rates of approximately 10% in naive patients infected with HCV genotype 1, and above 50% in those infected with other genotypes. …”
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Classical eikonal from Magnus expansion by Joon-Hwi Kim, Jung-Wook Kim, Sungsoo Kim, Sangmin Lee

“…Importantly, the Magnus expansion yields a finite eikonal, while the naïve eikonal based on the time-symmetric propagator is infrared-divergent from 3PM on.…”
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Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. These algorithms are used to build models with stacking and mixing techniques. …”
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Unveiling inverted D genes and D-D fusions in human antibody repertoires unlocks novel antibody diversity by Ponraj Prabakaran, Abhinav Gupta, Sambasiva P. Rao, Deepak Rajpal, Maria Wendt, Yu Qiu, Partha S. Chowdhury

“…We identified 25 unique inverted D genes (InvDs) in both naive and memory B cells from antibody repertoires of 13 healthy donors. …”
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Comparison of Continuous-Time Partial Markov Chain Construction by Heuristic Algorithms for Purpose of Approximate Transient Analysis by Eimutis Valakevičius, Mindaugas Bražėnas, Tomas Ruzgas

“…We consider three groups of such algorithms: naive, based on state characterization by the shortest path (obtained by Dijkstra method) and based on exact/approximate state probabilities. …”
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Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques. by Mahade Hasan, Farhana Yasmin, Md Mehedi Hassan, Xue Yu, Soniya Yeasmin, Herat Joshi, Sheikh Mohammed Shariful Islam

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”
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