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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Mapping China Aquaculture Ponds: Integrating a New Aquaculture Index With Machine Learning by JianChun Chen, Chen Lin, Kun Xue, Ke Song, ZhiGang Cao, RongHua Ma, DanHua Ma, YiJun Tong

“…(b) At the national scale, the spatial distribution of AP shows a pattern of higher density in the north and lower density in the south, with more AP in the east than in the west. …”
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Machine learning based disruption prediction using long short-term memory in KSTAR by Jeongwon Lee, Jayhyun Kim, Jinsu Kim, Sang-hee Hahn, Hyunsun Han, Giwook Shin, Yong-Su Na, Yong Un Nam

“…This study presents a machine learning model for predicting plasma disruptions using the KSTAR database. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. …”
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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

“…The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. …”
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MODELING AND ANALYSIS OF DYNAMICS IN BEARING SYSTEM OF DRILLING, MILLING AND BORING MACHINE WITH MONO-COLUMN by Yu. V. Vasilevich, S. S. Dovnar, A. S. Truskovsky, I. I. Shumsky

“…FEM-analysis of dynamics in the bearing system of a heavy-duty machine tool has been carried out in the paper. This is a prospective variant for a large-size multi-purpose machine tool with a horizontal sliding ram. …”
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Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases by Roshan Kumar, V Srirama, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga

“…Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. …”
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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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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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