Evaluating Customer Segmentation Techniques in the Retail Sector by Nur Diyabi, Duygu Çakır, Ömer Melih Gül, Tevfik Aytekin, Seifedine Kadry

“…This study evaluates the effectiveness of four machine learning algorithms (K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Self-Organizing Maps (SOM)) for customer segmentation in the Turkish retail market. …”
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Modeling sunflower yield and soil water–salt dynamics with combined fertilizers and irrigation in saline soils using APSIM and deep learning by Qingfeng Miao, Dandan Yu, Haibin Shi, Zhuangzhuang Feng, Weiying Feng, Zhen Li, José Manuel Gonçalves, Isabel Maria Duarte, Yuxin Li

“…Based on sunflower field experiments, four machine learning models (regression trees, random forest, support vector machines, and XGBoost) and two deep learning models (deep neural networks and neural networks) were developed to predict soil salinity. …”
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Intelligent design and synthesis of energy catalytic materials by Linkai Han, Zhonghua Xiang

“…We describe the workflow for designing and synthesizing new materials using machine learning (ML) and robotics. We summarize the sources of data collection, the intelligent algorithms commonly used to build ML models, and the laboratory modules for the intelligent synthesis of materials. …”
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Integrating distributed photovoltaic and energy storage in 5G networks for sustainable IoT applications by Yi Zhong, Yiting Wu, Yi Zheng, Xiaohu Ge

“…This paper explores the integration of distributed photovoltaic (PV) systems and energy storage solutions to optimize energy management in 5G base stations. By utilizing IoT characteristics, we propose a dual-layer modeling algorithm that maximizes carbon efficiency and return on investment while ensuring service quality. …”
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A Comprehensive Review of Quality Control and Reliability Research in Micro–Nano Technology by Nowshin Sharmile, Risat Rimi Chowdhury, Salil Desai

“…Advanced reliability testing and modeling approaches are highlighted for their effectiveness in predicting performance and mitigating risks. …”
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A novel canopy water indicator for UAV imaging to monitor winter wheat water status by Meiyan Shu, Zhenghang Ge, Yang Li, Jibo Yue, Wei Guo, Yuanyuan Fu, Ping Dong, Hongbo Qiao, Xiaohe Gu

“…To develop robust estimation models, four machine learning algorithms were implemented across individual and combined growth stages, and their performance was validated using independent ground-measured datasets that were not used during the training process. …”
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Exploring the relationship between data sample size and traffic flow prediction accuracy by Jianhu Zheng, Minghua Wang, Mingfang Huang

“…Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. …”
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Deep neural networks excel in COVID-19 disease severity prediction—a meta-regression analysis by Márton Rakovics, Fanni Adél Meznerics, Péter Fehérvári, Tamás Kói, Dezső Csupor, András Bánvölgyi, Gabriella Anna Rapszky, Marie Anne Engh, Péter Hegyi, Andrea Harnos

“…We identified both the relevant confounding factors of tool performance using the MetaForest algorithm and the best tools—comparing linear, machine learning, and deep learning methods—with mixed-effects meta-regression models. …”
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Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O by Johann Christopher Engster, Tobias Reinberger, Nele Blum, Pascal Stagge, Thorsten M. Buzug, Zouhair Aherrahrou, Maik Stille

“…To study the etiology of plaque progression, atherosclerotic mouse models are widely used. Traditionally, analyzing the obtained histological whole slide images of Oil Red O-stained aortic roots required manual segmentation. …”
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An Artificial Intelligence Pipeline for Hepatocellular Carcinoma: From Data to Treatment Recommendations by Zhang X, Yang L, Liu C, Yuan X, Zhang Y

“…In drug development, AI accelerates the discovery of new treatments through virtual screening and molecular modeling, while also identifying potential new uses for existing drugs. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) also showed promising results, with some models achieving up to 100% sensitivity in specific classifications. …”
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AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models by Xukuan Xu, Donghui Li, Jinghou Bi, Michael Moeckel

“…Abstract Design of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. …”
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Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

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Prediction of ischemic stroke in patients with H-type hypertension based on biomarker by Ke Chen, Jianxun He, Lan Fu, Xiaohua Song, Ning Cao, Hui Yuan

“…The optimal model, named A2BC ischemic stroke model, showed good discrimination and calibration ability for ischemic stroke with AUC of 0.91 and 0.87 in the internal and external validation cohorts. …”
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FOX-TSA hybrid algorithm: Advancing for superior predictive accuracy in tourism-driven multi-layer perceptron models by Sirwan A. Aula, Tarik A. Rashid

“…Nature-inspired optimization models have received a great deal of interest due to the performance of these algorithms in solving resourceful and authentic problems. …”
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Novel composite health assessment risk model for older allogeneic transplant recipients: BMT-CTN 1704 by Mohamed L. Sorror, Wael Saber, Brent Logan, Nancy Geller, Anna Bellach, Jianqun Kou, William Wood, John M. McCarty, Thomas G. Knight, Lyndsey Runaas, Laura Johnston, Jeremy Walston, Ryotaro Nakamura, Lori Jarrett, Asmita Mishra, Joseph Uberti, Parastoo B. Dahi, Jennifer N. Saultz, Shannon R. McCurdy, Lawrence E. Morris, Philip H. Imus, William J. Hogan, Kalyan Nadiminti, Vijaya Raj Bhatt, Rebecca Olin, Joseph Maakaron, Ronald Sobecks, Sarah A. Wall, Deborah Mattila, Bailey Protz, Steven M. Devine, Mary M. Horowitz, Andrew S. Artz

“…Two machine learning (ML) models (Cox and pseudo-value boosting) were also explored. …”
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Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis by Dan Wang, Rujie Huo, Lu Ye

“…Lethality-related m7G signature genes were then screened using machine learning methods, followed by the construction of a survival recognition model. …”
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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

“…However, these experiments are resource intensive. Machine learning (ML) models present a faster, cost-effective alternative for simulating outcomes and exploring various scenarios. …”
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Power flow analysis and volt/var control strategy of the active distribution network based on data-driven method by Chen Hui, Zhu Weiping, Liu Liguo, Shi Mingming, Xie Wenqiang, Zhang Chenyu

“…Secondly, the influence of PhotoVoltaic (PV) power supply on the distribution network is analyzed, and the volt/var optimization model based on PV power supply is proposed. …”
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A Novel Metaheuristic-Based Methodology for Attack Detection in Wireless Communication Networks by Walaa N. Ismail

“…Additionally, an optimized attention-based XGBoost classifier is utilized to improve model performance by combining the benefits of parallel gradient boosting and attention mechanisms. …”
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