Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. …”
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Physical Property Calculation and Refrigeration Cycle Analysis of Mixed Refrigerant R32/R290 by Jindong Zhang, Haixian Zeng, Daniel Djeuda Djapa, Blaise Kevin Rugwizangoga

“…This study investigates a binary mixture of R32 and R290 as a potential replacement for R410A. …”
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An Integrated Methodological Approach for Interpreting Used Oil Analysis in Diesel Engines by Reinaldo Ramirez Camba, Cristian Garcia Garcia, Milton Garcia Tobar, Jorge Fajardo Merchan

“…The framework includes critical limits for key lubricant parameters, correlation analysis, and Principal Component Analysis (PCA) to identify dominant degradation mechanisms. The Binary Segmentation (BS) algorithm is also used for Change-Point Detection. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…The results of this research not only illuminate the optimal scaling methods and ML models for forecasting heart disease, but also offer valuable perspectives on the pragmatic ramifications of implementing these models within a healthcare environment. …”
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Navigating Coastal Vulnerability: Introducing the Coastal Fuzzy Vulnerability Index (CFVI) by Zekâi Şen

“…Further research possibilities are also mentioned throughout the text and in the Conclusion section.…”
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Kinesiophobia and associated factors among patients after cardiac surgery under cardiopulmonary bypass: a cross-sectional study by Min You, Min You, Qianqian Mou, Qianqian Mou, Xiaotao Cao, Xiaotao Cao, Meng Luo, Meng Luo, Yi Song, Yi Song, Qingqing Li, Qingqing Li, Huiyun Hu, Huiyun Hu, Jianping Song

“…Healthcare providers should prioritize patients with the above risk profiles, while also establishing financial/emotional support systems, improving patient-family communication, optimizing acute pain management, implementing Enhanced Recovery After Cardiac Surgery protocols, and leveraging technology to design rehabilitation programs. …”
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Practicality of training a quantum-classical machine in the noisy intermediate-scale quantum era by Tarun Dutta, Alex Jin, Clarence Liu Huihong, José Ignacio Latorre, Manas Mukherjee

“…Our results provide insights into optimizing hybrid quantum-classical systems, emphasizing the importance of training strategies and hardware design. …”
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Analysis of artificial intelligence-discovered patterns and expert-designed aging patterns for 0.2 % proof stress in Ni-Al alloys with γ – γ' two-phase structure by Vickey Nandal, Sae Dieb, Dmitry S. Bulgarevich, Toshio Osada, Toshiyuki Koyama, Satoshi Minamoto, Masahiko Demura

“…In this current study, we continued our extensive exploration of AI methodologies, uncovering diverse patterns that also surpassed the isothermal aging benchmark. Remarkably, out of 2823 NIA schedules, we found 173 ones outperforming the isothermal aging benchmark. …”
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Research on the Timing of Replacing Worn Milling Cutters by Using Wear Transition Percentage Constructed Based on Spindle Current Clutter Signals by Zhihao Liu, Min Wang, Zhishan Wang, Tao Zan, Xiangsheng Gao, Peng Gao

“…The use of worn cutters not only reduces the machining accuracy but also increases the surface roughness. Therefore, it is important for enterprises to establish replacement rules for worn cutters. …”
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Anchor Dragging Risk Estimation Strategy from Supervised Cost-Sensitive Learning by Sang-Lok Yoo, Shem Otoi Onyango, Joo-Sung Kim, Kwang-Il Kim

“…This suggested that the optimal ML strategy would involve undersampling the majority class and cost-sensitive learning. …”
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Image Processing Method Based on Chaotic Encryption and Wavelet Transform for Planar Design by Yiying Liu, Young Chun Ko

“…Firstly, the traditional Mallat algorithm is optimized; since the mean value of the transform coefficients generated after the wavelet transform of the image is used as the initial value of the chaotic system to iterate, when the image is modified, then the mean value of the wavelet coefficients will also change, and the final iteration comes out as two different sequences using the property that the chaotic system is extremely sensitive to the initial conditions, so the algorithm has a certain sensitivity to tampering and localization effect. …”
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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo Dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz Dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

“…After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. Data was also correlated, balanced, and divided into training and test portions for application in machine learning models. …”
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A Reconfigurable 1x2 Photonic Digital Switch Controlled by an Externally Induced Metasurface by Alessandro Fantoni, Paolo Di Giamberardino

“…This dataset has been used for training and testing of a machine learning algorithm for the classification of the MMI configuration in terms of binary digital output for a 1x2 switch. Also, a statistical analysis has been produced, targeting the definition of the most incident-activated pixel for each switch operation. …”
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A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images by Francesca Arezzo, Annarita Fanizzi, Rosanna Mancari, Emiliano Cocco, Samantha Bove, Maria Colomba Comes, Mariangela Gianciotta, Giorgia Lanza, Salvatore Lopez, Gerardo Cazzato, Erica Silvestris, Elsa Vitale, Enrico Vizza, Gennaro Cormio, Raffaella Massafra, Vera Loizzi

“…In addition, to assess the effect of the potential added value of using this tool in clinical practice, we compared the operator’s performance with and without the developed automated support. The binary and multiclass model reached optimal classification performances with a mean AUC value equals to 90.76 (88.63–92.89 IC95%). …”
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Strategies for passive thermal management of lithium-ion batteries in microgravity: Combining PCMs, metal foams, fins, and nanoparticles by Hamid-Reza Bahrami, Mahziyar Ghaedi

“…To address the low thermal conductivity of PCMs, additional strategies such as the inclusion of fins, nanoparticles, and porous foam were systematically examined. Binary and ternary combinations of these methods were also analyzed to identify optimal configurations. …”
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Effect of information-embedded policy tools on farmers’ low-carbon production behavior: Analysis based on a randomized controlled trial with Chinese medicinal herb growers in Qinli... by LI Huanli, XUE Caixia, LI Fuduo, JIANG Xuan

“…An in-depth investigation into the effect of policy tools on farmers’ low-carbon production behavior can not only evaluate the effectiveness of existing policies but also provide data support for future policy adjustments. …”
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A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection by Murat Sarıateş, Erdal Özbay

“…Conclusions: This study presents an optimized FT-EPN model to enhance the performance of DL models for PCa classification, offering a reference solution for future research. …”
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Electrostatic Attraction‐Driven Assembly of Non‐Noble Metallo‐Supramolecular Polymers With Single‐Walled Carbon Nanotubes for Boosting Photocatalytic Hydrogen Evolution by Yanyan Qin, Chen Zhang, Yidi Wang, Pengfei She, Wai‐Yeung Wong

“…The optimized CNT@Se‐CoTpy composite, containing 1.2 wt% metal loading, achieves 7 times higher hydrogen evolution rate (2.47 mmol g−1 h−1) than bare Se‐CoTpy (0.35 mmol g−1 h−1). …”
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Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…A web platform ( https://prediction-killip-gby.streamlit.app/ ) was also developed to facilitate clinical application. …”
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Segmentation for mammography classification utilizing deep convolutional neural network by Dip Kumar Saha, Tuhin Hossain, Mejdl Safran, Sultan Alfarhood, M. F. Mridha, Dunren Che

“…We also compared the performance of the proposed model with other transformer model vision transformers (ViT) and DL models, MobileNetV3 and EfficientNetB7, respectively. …”
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