Prediction of laser welding qualities of Al alloys using regression and machine learning techniques by Hemant Kumar, Soumyabrata Chakravarty, Nitesh Kuamr, Nikhil Kumar

“…This work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. …”
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Domain Knowledge Preservation in Financial Machine Learning: Evidence from Autocallable Note Pricing by Mohammed Ahnouch, Lotfi Elaachak, Erwan Le Saout

“…The component-wise analysis reveals complex interactions between autocall mechanisms and higher-order sensitivities, particularly affecting vanna and volga patterns near barrier levels. These findings provide empirical evidence that financial machine learning benefits from domain-specific feature engineering principles that preserve economic relationships. …”
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Planning and layout of tourism and leisure facilities based on POI big data and machine learning. by Shifeng Wu, Jiangyun Wang, Yinuo Jia, Jintian Yang, Jixiu Li

“…Drawing on POI and demographic data, and considering the distribution patterns of existing tourism and leisure facilities, this research applies machine learning to quantitatively simulate the optimal siting of such amenities. …”
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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

“…However, traditional prediction methods based on mathematical models have limitations in capturing complex subsidence patterns. Machine learning techniques have shown promise in enhancing subsidence prediction accuracy. …”
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A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing by In-Bae Lee, Youngjin Kim, Sojung Kim

“…Autonomous decision-making algorithms are devised to recognize incorrect patterns of PV modules in terms of product quality. …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. …”
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BiAF: research on dynamic goat herd detection and tracking based on machine vision by Yun Hou, Mingjuan Han, Wei Fan, Xinyu Jia, Zhuo Gong, Ding Han

“…Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. …”
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Machine learning based anoikis signature predicts personalized treatment strategy of breast cancer by Xiao Guo, Jiaying Xing, Yuyan Cao, Wenchuang Yang, Xinlin Shi, Runhong Mu, Tao Wang

“…Anoikis, a form of programmed cell death critical in preventing metastasis, plays a pivotal role in breast cancer progression.MethodsThis study introduces the Artificial Intelligence-Derived Anoikis Signature (AIDAS), a novel machine learning-based prognostic tool that identifies key anoikis-related gene patterns in breast cancer. …”
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Influenza virus genotype to phenotype predictions through machine learning: a systematic review by Laura K. Borkenhagen, Martin W. Allen, Jonathan A. Runstadler

“…Background: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. …”
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Exploring New Paradigms in Time Series Prediction by Integrating Computer Simulations and Machine Learning by Pengcheng Wu, Yan Shi, Pengfei Zhao, Zhengzhao Gu

“…Empirical evaluations across diverse benchmark datasets validate the effectiveness of our framework, demonstrating its robustness, scalability, and ability to uncover meaningful temporal patterns in complex time series data.…”
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Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning by Giulio Angiolini, Giovanna Maria Dimitri

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A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning by Shilan Abdullah Hassan, Maha Sabah Saeed

“…By leveraging Azure Machine Learning's capabilities, researchers and healthcare professionals can analyze large datasets containing patient information and medical records to identify patterns and risk factors associated with heart attacks. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

“…<b>Background</b>: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. …”
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Cobb angle prediction for adolescent idiopathic scoliosis via an explainable machine learning model by Yu Ding, Bin Li, Xiaoyong Guo

“…This study aims to build an accurate and interpretable machine learning model capable of adolescent idiopathic scoliosis prognostication. …”
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Machine learning-based analysis of defensive strategies in basketball using player movement data by Jianlong Li

“…The proposed hybrid model integrates LSTM to identify temporal features of players’ motion and CNN to recognize spatial patterns across the court. The model was trained and evaluated on the National Basketball Association’s (NBA) SportVU tracking data, which includes over 32,000 possessions. …”
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Machine Learning for Resilient and Sustainable Cities: A Bibliometric Analysis of Smart Urban Technologies by Bin Luan, Xinqun Feng

“…This study aims to conduct a bibliometric analysis of the published research in the fields of smart cities and machine learning, using visualization techniques to reveal the spatiotemporal distribution patterns, research hotspots, and collaborative network structures. …”
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Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach. by Saman Hosseini Ashtiani, Sarah Akel, Rakesh Kumar Banote, Fredrik Asztely, Johan Zelano

“…An interesting question is whether biomarker patterns could contribute additional understanding compared to individual marker values. …”
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A bibliometric analysis of artificial intelligence and machine learning applications for human resource management by Güler Koştı, İsmail Kayadibi

“…Abstract This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. …”
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Early-stage detection of maize seed germination based on RGB image and machine vision by Xiaohang Liu, Zhao Zhang, Yunxia Li, C. Igathinathane, Jiangfan Yu, Zhaoyu Rui, Afshin Azizi, Xiqing Wang, Alireza Pourreza, Man Zhang

“…Collected color (RGB) images of germination trays planted with maize seeds sown in preset patterns were preprocessed as regions of interest (RoI) for each seed for analysis. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. …”
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