Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. …”
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Machine learning-based prediction and classification of seawater intrusion in the hyper-arid coastal aquifer of Fujairah, UAE by Assaad Kassem, Ahmed Sefelnasr, Abdel Azim Ebraheem, Luqman Ali, Faisal Baig, Mohsen Sherif

“…Study focus: Fifteen machine learning (ML) algorithms were evaluated to predict and classify total dissolved solids (TDS) as an indicator of SWI. …”
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The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches by Lu Zhang, Lu Zhang, YuJing Lv, Mengqing Ma, Jile Lv, Jie Chen, Shang Lei, Yi Man, Guimei Xing, Yu Wang

“…Three hub genes (JDP2, ALOX5, and KPNB1) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. …”
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Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm by El Assri Nasima, Ennejjar Mohammed, Jallal Mohammed Ali, Chabaa Samira, Zeroual Abdelouhab

“…The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). …”
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Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

“…In addition, the groundwater recharge is being afftected by shifting land use pattern caused by urban development. Using precise and trustworthy estimates of groundwater level is vital for the sustainable groundwater resources management in the face of changing climatic circumstances. …”
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Evaluation of K-Means Algorithm for Faulted Landforms Extraction and Offset Measurement With an Example From the Eastern Kunlun Fault by Shengchao Zhou, Zhou Lin

“…Although supervised deep learning methods have great potential for image recognition and segmentation, due to the absence of data sets, we apply the K-means algorithm, an easy and practical unsupervised machine learning method with minimal parameters, to extract displaced geomorphic markers. …”
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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

“…Eventually, the whale optimization algorithm (WOA) is used to optimally choose the hyperparameters of the CNN‐BiGRU model. …”
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Fault Diagnosis of Train Bogie Bearing Based on Multi-scale Sample Entropy Improved Extreme Learning Machine by JIN Zhenzhen, HE Deqiang, MIAO Jian, XU Weichang

“…Finally, the feature vector set is divided into test set and training set, and the improved extreme learning machine is used as a pattern recognition algorithm for fault pattern recognition. …”
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Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. …”
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Advanced machine learning technique for solving elliptic partial differential equations using Legendre spectral neural networks by Ishtiaq Ali

“…In this work, a novel approach based on a single-layer machine learning Legendre spectral neural network (LSNN) method is used to solve an elliptic partial differential equation. …”
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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…This study, through the integrated application of computational biology and machine learning algorithms, discovered biomarkers of PCD patterns that affect cytokine storm-mediated inflammation and immunosuppressive effects in sepsis populations across different age groups (neonates, children, and adults). …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…To simplify the analysis and reduce computational complexity, a subset of key spectral wavelengths was selected using a successive projection algorithm. Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. …”
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Clinical Characterization of Patients with Syncope of Unclear Cause Using Unsupervised Machine-Learning Tools: A Pilot Study by María-José Muñoz-Martínez, Manuel Casal-Guisande, María Torres-Durán, Bernardo Sopeña, Alberto Fernández-Villar

“…This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. …”
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Machine Learning-Based Differential Diagnosis of Parkinson’s Disease Using Kinematic Feature Extraction and Selection by Masahiro Matsumoto, Abu Saleh Musa Miah, Nobuyoshi Asai, Jungpil Shin

“…Initially, 18 kinematic features are extracted, including two newly proposed features: Thumb-to-index vector velocity and acceleration, which provide insights into motor control patterns. In addition, 41 statistical features were extracted here from each kinematic feature, including some new approaches such as Average Absolute Change, Rhythm, Amplitude, Frequency, Standard Deviation of Frequency, and Slope. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. …”
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Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

“…Understanding environmental factors that influence corn yield is crucial for improving crop management and designing more resilient cropping systems. Leveraging machine learning (ML) techniques capable of handling large-scale datasets offers a promising alternative for uncovering hidden patterns and generating actionable insights to improve crop yield. …”
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Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis by Deepak Kumar, Brijesh Bakariya, Chaman Verma, Zoltán Illés

“…The study was conducted on collected serum biomaker samples of 81 patients with Inflammatory Bowel Disease (IBD) of Changhua Christian Hospital in China, including 36 with Crohn’s disease (CD) and 45 with Ulcerative Colitis (UC) using Latent Semantic Analysis(LSA) and machine learning (ML) techniques.Machine Learning algorithms Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), and Support Vector Classifier (SVC), were utilized to predict liver risk associated with conditions including Hepatitis, Autoimmune Hepatitis (AIH), Alcoholic Liver Disease (ALD), and Non-Alcoholic Fatty Liver Disease (NAFLD). …”
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Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

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Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation by Ge Y, Liu X, Shu J, Jiang X, Wu Y

“…We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

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