Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…Various machine learning algorithms were used to construct radiomics, deep learning, and combined models. …”
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An ensemble-driven machine learning framework for enhanced water quality classification by Preet Singh, Taniya Hasija, Salil Bharany, Hafiza Nazra Tun Naeem, B. Chinna Rao, Seada Hussen, Ateeq Ur Rehman

“…Its accurate definition helps identify health risks, optimize resource consumption, and feed sustainable practices. This study applies machine learning (ML) models to classify water quality using an integrated dataset from Telangana, India. …”
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Machine Learning Analysis of Arterial Oscillograms for Depression Level Diagnosis in Cardiovascular Health by Vladislav Kaverinsky, Dmytro Vakulenko, Liudmyla Vakulenko, Kyrylo Malakhov

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Global trends in machine learning applications for single-cell transcriptomics research by Xinyu Liu, Zhen Zhang, Chao Tan, Yinquan Ai, Hao Liu, Yuan Li, Jin Yang, Yongyan Song

“…Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized cellular heterogeneity analysis by decoding gene expression profiles at individual cell level, while machine learning (ML) has emerged as core computational tool for clustering analysis, dimensionality reduction modeling and developmental trajectory inference in single-cell transcriptomics(SCT). …”
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Science Through Machine Learning: Quantification of Post‐Storm Thermospheric Cooling by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, Douglas P. Drob, W. Kent Tobiska, Jean Yoshii

“…Abstract Machine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. …”
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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. …”
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Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application by Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

“…IntroductionThis paper addresses the growing intersection of machine learning (ML) and psychotherapy by developing a classification model for analyzing topics in therapist remarks. …”
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Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates by N. Ragu Ramalingam, K. Johnson, M. Pagani, M. Pagani, M. L. V. Martina

“…These simulation results are fit using a machine learning (ML)-based variational encoder–decoder model. …”
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Tether Force Estimation Airborne Kite Using Machine Learning Methods by Akarsh Gupta, Yashwant Kashyap, Panagiotis Kosmopoulos

“…Through a series of controlled field experiments and the application of classical machine learning techniques, we achieved significant improvements in tether force estimation. …”
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Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation by Amirmasoud Amiran, Behrouz Behnam, Sanaz Seyedin

“…This research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. …”
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Dynamic bandwidth allocation with machine learning in dense WiFi network by Ricardo Alvarado, Bayron Opina, Johan Tellez, Vivian Triana

“…This document introduces the application of a machine learning-based prediction model to outline time intervals of congestion in a densely populated WiFi network employing dynamic load balancing. …”
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System-Level Optimization in Switched Reluctance Machine Design—Current Trends, Methodologies, and Future Directions by Aristotelis Tzouvaras, Georgios Falekas, Athanasios Karlis

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Determination of cervical vertebral maturation using machine learning in lateral cephalograms by Shahab Kavousinejad, Asghar Ebadifar, Azita Tehranchi, Farzan Zakermashhadi, Kazem Dalaie

“…This study aimed to develop a semi-automated approach using machine learning based on cervical vertebral dimensions (CVD) for determining skeletal maturation status. …”
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A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification by Fang He, Xiaoxia Wu, Wenyu Zhang, Xiaoling Huang

“…With the rapid development of machine learning in the field of classification, the classification fairness has become the research emphasis second to prediction accuracy. …”
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Optimising water use in copper flotation with the design of experiments and machine learning by Rachid El-Bacha, Abderrahim Salhi, Hafid Abderrafia, Souad Rabi

“…Through the design of experiments with the integration of machine learning, especially for the choice of the model, the optimal proportion was determined, which made it possible to achieve a metal recovery of more than 80% using a 50/50 mix of fresh and recycled water. …”
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Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Muhammad Ehsan Rana, Jimmy H. Gutiérrez-Bahamondes

“…Specifically, we employ the Light Gradient Boosting Machine (LightGBM) for predictive modeling, stochastic simulations to account for dynamic variables and competitive interactions, and SHapley Additive Explanations (SHAPs) to interpret model outputs at both the global and patient-specific levels. …”
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Study on the Impact of Input Parameters on Seawater Dissolved Oxygen Prediction Models by Wenqing Li, Jing Lv, Yuhang Wang, Xiangfeng Kong

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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. …”
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Measuring place rather than space: integrating landscape qualities for the urban–rural gradient using people’s perceptions and machine learning by Michal Switalski, Maarten Van Strien, Adrienne Grêt-Regamey

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