An assessment of the long-term change of the Mersin west coastline using digital shoreline analysis system and detection of pattern similarity using fuzzy C-means clustering by Ozcan Zorlu, Lutfiye Kusak

“…To identify spatial patterns in shoreline change, the Fuzzy C-Means (FCM) clustering algorithm was applied using the NSM, SCE, EPR, and LRR metrics. …”
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Real-Time Depth Monitoring of Air-Film Cooling Holes in Turbine Blades via Coherent Imaging During Femtosecond Laser Machining by Yi Yu, Ruijia Liu, Chenyu Xiao, Ping Xu

“…The demonstrated system represents an advancement in non-destructive in-process monitoring for high-precision laser machining applications.…”
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Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds. by Fei Cai, Yuehua Wei, Daniel Kirchhofer, Andrew Chang, Yingnan Zhang

“…We hypothesized that specific sequence patterns within the peptide scaffolds played a crucial role in spontaneous folding into a stable topology, and thus, these sequences should not be subject to randomization in the original library design. …”
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Decoding corporate communication strategies: Analysing mandatory published information under Pillar 3 across turbulent periods with unsupervised machine learning. by Anna Pilková, Michal Munk, Lívia Kelebercová

“…This study explores the communication patterns of Slovak banks with stakeholders through mandatory disclosures mandated by Basel III's Pillar 3 framework and annual reports in 2007-2022. …”
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Development of a flexible electronic control unit for seamless integration of machine vision to CAN-enabled boom sprayers for spot application technology by Mozammel Bin Motalab, Ahmad Al-Mallahi

“…The first used UART protocol to parse machine vision messages, detect pest areas, and convert them into binary arrays for nozzle activation. …”
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Exploring the Streetscape Perceptions from the Perspective of Salient Landscape Element Combination: An Interpretable Machine Learning Approach for Optimizing Visual Quality of Str... by Wanyue Suo, Jing Zhao

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A hybrid model integrating recurrent neural networks and the semi-supervised support vector machine for identification of early student dropout risk by Huong Nguyen Thi Cam, Aliza Sarlan, Noreen Izza Arshad

“…Methods A hybrid prediction model DeepS3VM is designed by integrating a Semi-supervised support vector machine (S3VM) model with a recurrent neural network (RNN) to capture sequential patterns in student dropout prediction. …”
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Machine learning-based spatio-temporal assessment of land use/land cover change in Barishal district of Bangladesh between 1988 and 2024 by Walida Zaman, H Rainak Khan Real

“…The performance of four machine learning algorithms (Support Vector Machine, Classification and Regression Tree, K-Nearest Neighbor, and Random Forests) were evaluated to ensure classification reliability. …”
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A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model by Juanru Zhao, Mei Yuan, Yiwen Cui, Jin Cui

“…In this paper, we propose a cross-machine IFD method based on a residual full convolutional neural network (ResFCN) transfer learning model, which leverages the time-series features of monitoring data. …”
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Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach by Daichi Sone, Noriko Sato, Yoko Shigemoto, Iman Beheshti, Yukio Kimura, Hiroshi Matsuda

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Integrating environmental and LULC drivers of groundwater droughts in groundwater-dependent ecosystems: a machine learning (XGBoost)-SEM analysis with ecosystem implications by Kawawa Banda, Christopher Shilengwe, Imasiku Nyambe

“…Results The study reveals that LULC types, particularly water bodies, cropland and bare land, exert the greatest influence on groundwater drought responses under teleconnection patterns attributed to ENSO, rather than through changes in the net water balance. …”
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Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang, Jeong-Jin Yu

“…Unsupervised learning revealed no distinct distribution patterns between patients with/without CAAs. <b>Conclusions</b>: Despite utilizing a large dataset to develop a machine learning-based prediction model for CAAs, the performance was unsatisfactory. …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…Abstract In this paper, three Double Machine Learning (DML) models are proposed to enhance the accuracy of breast cancer detection using machine learning techniques using breast cancer detection dataset. …”
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Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains by Kaizhe Jin, Adrian Rubio-Solis, Ravi Naik, Daniel Leff, James Kinross, George Mylonas

“…Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. …”
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Integrated multi-omics and machine learning reveal an immunogenic cell death-related signature for prognostic stratification and therapeutic optimization in colorectal cancer by Siyu Hou, Shanshan Heng, Shaozhuo Xie, Yuanchun Zhao, Jiajia Chen, Chunjiang Yu, Yuxin Lin, Yuxin Lin, Xin Qi

“…Multidimensional analysis revealed significant associations between ICDRS-derived risk score and distinct immune infiltration patterns, immunotherapy response and TME characteristics. …”
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Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study by Nate C. Carnes, Claire A. Kolaja, Crystal L. Lewis, Sheila F. Castañeda, Rudolph P. Rull, for the Millennium Cohort Study Team

“…Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data in which respondents begin but do not finish a survey, that can lead to biased conclusions. …”
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A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE by Md. Shamshuzzoha, Tazkia Tasnim Bahar Audry, Md. Jahangir Alam, Zaheed Ahmed Bhuiyan, Md Motaharul Islam, Mohammad Mehedi Hassan

“…This study addresses these gaps by curating a unique social media dataset that captures seasonal patterns and employing advanced machine learning techniques for accurate SAD detection. …”
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Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort s... by Jia-Wei Feng, Lu Zhang, Yu-Xin Yang, Rong-Jie Qin, Shui-Qing Liu, An-Cheng Qin, Yong Jiang

“…Abstract Background This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. …”
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A Hybrid Sequential Feature Selection Approach for Identifying New Potential mRNA Biomarkers for Usher Syndrome Using Machine Learning by Rama Krishna Thelagathoti, Wesley A. Tom, Dinesh S. Chandel, Chao Jiang, Gary Krzyzanowski, Appolinaire Olou, M. Rohan Fernando

“…The ddPCR results were consistent with expression patterns observed in the integrated transcriptomic metadata, reinforcing the credibility of our machine learning-driven biomarker discovery framework. …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…By collecting and analyzing routine blood data, machine learning models were built to identify the patterns of changes in blood indicators related to HF.MethodsWe conducted a statistical analysis of routine blood data from 226 patients who visited Zhejiang Provincial Hospital of Traditional Chinese Medicine (Hubin) between May 1, 2024, and June 30, 2024. …”
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