Neutrosophic Statistical Regression Models for Predicting the Incidence of Nosocomial Infections in Post-Trauma Patients by Nairovys Gómez Martinez, Gerardo Ramos Serpa, Riber Fabian Donoso Noroña, Gloria Rebeca Medina Naranjo

“…The results demonstrate the validity of statistical-neutrosophic models to acknowledge the complexities of clinician data to determine the best predictive outcomes. …”
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Data-Efficient Training of Gaussian Process Regression Models for Indoor Visible Light Positioning by Jie Wu, Rui Xu, Runhui Huang, Xuezhi Hong

“…A data-efficient training method, namely Q-AL-GPR, is proposed for visible light positioning (VLP) systems with Gaussian process regression (GPR). The proposed method employs the methodology of active learning (AL) to progressively update the effective training dataset with data of low similarity to the existing one. …”
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RSA modulus length regression prediction based on the Run Test and machine learning in the ciphertext-only scenarios by Ke Yuan, Chenmeng Zhao, Longwei Yang, Hanlin Sun, Sufang Zhou, Chunfu Jia

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Analysis of Land Surface Temperature Driving Factors and Spatial Heterogeneity Research Based on Geographically Weighted Regression Model by Yin Zhi, Liang Shan, Lina Ke, Ruxin Yang

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Ridge Regressive Data Preprocessed Quantum Deep Belief Neural Network for Effective Trajectory Planning in Autonomous Vehicles by S. Nirmala Devi, Rajesh Natarajan, Gururaj H. L., Francesco Flammini, Badria Sulaiman Alfurhood, Sujatha Krishna

“…To address these problems, the Ridge Regressive Data Preprocessed Quantum Deep Belief Neural Network (RRDPQDBNN) model is developed. …”
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Filter Learning-Based Partial Least Squares Regression and Its Application in Infrared Spectral Analysis by Yi Mou, Long Zhou, Weizhen Chen, Jianguo Liu, Teng Li

“…Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. …”
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Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer. by Silvia Pineda, Francisco X Real, Manolis Kogevinas, Alfredo Carrato, Stephen J Chanock, Núria Malats, Kristel Van Steen

“…Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. …”
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A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone by Seung-Won Seo, Gyumin Choi, Ho-Jin Jung, Mi-Jin Choi, Young-Dae Oh, Hyun-Seok Jang, Han-Kyu Lim, Seongil Jo

“…This approach accommodates heteroscedasticity, capturing varying levels of variance across observations, and models complex, non-linear relationships between environmental factors and abalone growth. …”
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Integration of Artificial Neural Network Regression and Principal Component Analysis for Indoor Visible Light Positioning by Negasa Berhanu Fite, Getachew Mamo Wegari, Heidi Steendam

“…ANNs excel at modeling the intricate relationships within data, making them well-suited for handling the complex dynamics of indoor lighting environments. …”
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Machine learning regression based quantification of dynamic movements of prokaryotic bacterial type IV pili. by Isha Kanchana

“…This study demonstrates the potential of automated image analysis techniques to expedite the quantification of complex bacterial behaviors, such as T4P dynamics while maintaining high accuracy. …”
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REGRESSION ANALYSIS AND GIS METHODS FOR ASSESSMENT OF THE PLAGUE EPIZOOTIC ACTIVITY OF KAZAKH NATURAL PLAGUE FOCI by N. .. Niyazbekov, Z. .. Abdeliyev, Z. .. Sagiyev, A. .. Matzhanova, A. .. Abdrazakova, B. .. Aysauytov, M. .. Balibayev, N. .. Toksanbayeva, Sh. .. Ibrayeva, A. .. Yermakhanov, M. .. Burambayeva, S. .. Zhadyrassyn, B. .. Aimakhanov, L. .. Kupteleuova, K. .. Akmambetova

“…The plague natural foci of Kazakhstan are a complicated system of relations between the plague microbe, warm-blood host and vector. The complex approaches used for study of the processes of the plague epizooty and prognosis it. …”
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On physical analysis of topological indices and entropy measures for porphyrazine structure using logarithmic regression model by Asma Khalid, Shoaib Iqbal, Muhammad Kamran Siddiqui, Tariq Javed Zia, Brima Gegbe

“…Furthermore, establishing correlations between these indices and entropy using logarithmic regression models allows for a deeper understanding of complex properties of porphyrazine. …”
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Comparative Analysis of Regression Models for Stock Price Prediction: Linear, Support Vector, Polynomial, and Lasso by Ștefan Rusu, Marcel Ioan Boloș, Marius Leordeanu

“…Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.…”
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Comparative analysis of the performance of regression machine learning models for indoor visible light positioning systems by Mohamed Hussien Moharam

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Integration of Regression-Based Guidance Ant for Enhanced Exploration and Convergence in Ant Colony Optimization (ACO) by Desi W. Sari, Suci Dwijayanti, Bhakti Y. Suprapto

“…These findings highlight the effectiveness of regression-based guidance in improving the performance of the ACO algorithm, making it more suitable for real-time autonomous vehicle navigation in complex environments. …”
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Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model by Haitao Chen, Nishi Chu, Aiqing Kang, Wenchuan Wang, Ji He

“…However, modeling ET in arid grasslands faces significant challenges due to data scarcity, high spatiotemporal heterogeneity, and complex interactions among climatic drivers. To address these challenges, this study developed a Random Forest Regression (RF-R) model integrated with high-resolution PML-V2 ET data and CRU meteorological datasets (2001–2020) to simulate ET in China’s desert grasslands. …”
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CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION by Steven Soewignjo, Ni Wayan Widya Septia Sari, Andini Putri Mediani, M. Aqil Zaidan Kamil, Dita Amelia, Nur Chamidah

“…This study aims to forecast the exchange rate between the Chinese Yuan (CNY) and the Indonesian Rupiah (IDR) using Support Vector Regression (SVR), a machine-learning technique that can handle nonlinear and complex data. …”
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Comparative Analysis of Artificial Neural Networks with Classical Regression Models for Predicting Dissolved Oxygen in Water by Ana Ivette Jater Ruiz, Francisco Primero Primero, Roberto Alejo Eleuterio, Francisco Javier Illescas Martínez, Federico Del Razo López, Everardo Granda

“…Our analysis shows that a Multi-Layer Perceptron (MLP) outperforms traditional regression approaches. The MLP effectively captures complex, nonlinear relationships in water quality data, achieving higher predictive accuracy as measured by the coefficient of determination (R²). …”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. …”
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A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau by Ritu Wu, Zhimin Hong, Wala Du, Yu Shan, Hong Ying, Rihan Wu, Byambakhuu Gantumur

“…In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. …”
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