Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform by Fengjun Qi, Zhenping Liu, Wenzheng Zhang, Zhenjie Sun

“…However, conventional regression models often rely on a shared input space for all targets, neglecting the fact that distinct and complex feature sets may influence each target. …”
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Investigation of key ferroptosis-associated genes and potential therapeutic drugs for asthma based on machine learning and regression models by Yuhang Chen, Jie Wang, Ye Zhang, Ziwei Zhang, Hong Chen, Jiaojiao Hu, Ke Zhu, Liqun Wu, Fangwei Xu

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Anthropometric and fitness predictors of operational preparedness among Malaysian firefighters: a clustering and multivariate logistic regression approach by Borhanudin Mohd Yusof Mohamed, Rabiu Muazu Musa, Mohamad Nizam Nazarudin, Anwar P. P. Abdul Majeed, Naresh Bhaskar Raj, Vijayamurugan Eswaramoorth

“…Modern firefighting now encompasses complex land, sea, and air rescue operations alongside conventional fire suppression, requiring rigorous preparedness.   …”
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Late‐Onset Crizotinib‐Associated Renal Cysts With Subsequent Regression Following Dose Reduction: A Case Report by Joon‐Young Yoon, Won Gi Jeong, Yoo‐Duk Choi, Seung Ji Kang, Hwa Kyung Park, Hyung‐Joo Oh, Cheol‐Kyu Park, In‐Jae Oh, Young‐Chul Kim

“…We report a case of a 68‐year‐old Korean male who developed complex renal cysts after 4 years of crizotinib therapy. …”
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Possible spontaneous regression of hepatocellular carcinoma with unique histopathological features confirmed by surgical resection: a case report by Toshihisa Kimura, Takanori Goi, Shigehiro Yokoi, Kenji Ohnishi, Tamotsu Togawa, Atsushi Iida, Makoto Ishida, Yasunori Sato

“…Conclusions We consider this case representative of spontaneous regression of HCC, and the immune response against HCC might contribute to tumor regression, leading to complex histopathological appearances. …”
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Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration by Runcheng Fang, Scott Collingwood, Yue Zhang, Joseph B. Stanford, Christina Porucznik, Darrah Sleeth

“…This study compares linear regression (LR) and machine learning (ML) techniques, particularly random forest (RF), to determine optimal calibration strategies. …”
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Effect of Substrate Temperature on Bead Track Geometry of 316L in Directed Energy Deposition: Investigation and Regression Modeling by Deviprasad Chalicheemalapalli Jayasankar, Stefan Gnaase, Dennis Lehnert, Artur Walter, Robin Rohling, Thomas Tröster

“…Future work will focus on refining the regression models to improve predictions, particularly for dilution, and further investigate non-linear interactions between process variables.…”
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Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression by Javad Artin, Amin Valizadeh, Mohsen Ahmadi, Sathish A. P. Kumar, Abbas Sharifi

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End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression by Chuang Gao, Minggang Shen, Xiaoping Liu, Lidong Wang, Maoxiang Chu

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Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression by Muhammad Shahzad Nazir, Sami ud Din, Wahab Ali Shah, Majid Ali, Ali Yousaf Kharal, Ahmad N. Abdalla, Padmanaban Sanjeevikumar

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Modeling the hillside movement in the area of Sattarkhan dam reservoir using by predictive models Logistic Regression and Neural Network by soghra andaryani, Nasrin Samandar, Ohammadreza Nikjoo

“…1-Introduction Evaluation of slope instabilities such as many of the most complex issues of environmental hazards are considered as the important factors in the occurrence of slope instability, mostly due to the variety of issues. …”
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A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression by Hilario Gómez-Moreno, Pedro Gil-Jiménez, Sergio Lafuente-Arroyo, Roberto López-Sastre, Saturnino Maldonado-Bascón

“…The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. …”
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A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. by Francesco Gregoretti, Vincenzo Belcastro, Diego di Bernardo, Gennaro Oliva

“…It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. …”
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Multi-Scale 3D Cephalometric Landmark Detection Based on Direct Regression with 3D CNN Architectures by Chanho Song, Yoosoo Jeong, Hyungkyu Huh, Jee-Woong Park, Jun-Young Paeng, Jaemyung Ahn, Jaebum Son, Euisung Jung

“…However, 3D imaging introduces challenges due to increased network complexity and computational demands. This study proposes a multi-scale 3D CNN-based approach utilizing direct regression to improve the accuracy of maxillofacial landmark detection. …”
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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…This study compares three regression algorithms—Linear Regression, Random Forest, and XGBoost—to evaluate their effectiveness in modeling the complexity of socio-economic data. …”
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Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning by YANG Yinghe, WEI Handi, FAN Dixia, LI Ang

“…In order to overcome the complexity and variability of underwater working environments, as well as the difficulty of controlling the flapping motion due to the significant nonlinear characteristics and numerous variables involved, a direct exploration approach is proposed to search for the optimal flapping foil propulsion parameters in the environment. …”
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The determinants of enterprise risk management in Ghanaian and South African insurers: insights from a quantile regression analysis by Sylvester Senyo Horvey

“…Accordingly, this study recruits panel logistic regression to examine the factors influencing the adoption of ERM. …”
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Medication adherence among Jordanian adults with chronic conditions: a combined analysis using regression and machine learning by Walid Al-Qerem, Anan Jarab, Judith Eberhardt, Salwa Abdo, Lujain Al-sa’di, Razan Al-Shehadeh, Dana Khasim, Ruba Zumot, Sarah Khalil

“…Quantile regression and machine learning models were applied.Results A total of 981 patients (63.1% females) were enrolled in the study. …”
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Prediction of building subsidence in Vietnam using machine learning techniques based on leveling results by Dinh Trong Tran, Ngoc Dung Luong, Dinh Huy Nguyen

“…We utilize a dataset from a subsidence monitoring network in Hoa Binh General Hospital and compare the performance of linear regression, decision tree regression, and random forest regression models. …”
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Unveiling the black box: understanding digital assets' complexity by Raghda Abdellatif Abdelkhalik Elsayed

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