Comparative Analysis of Voting and Stacking Ensemble Learning for Heart Disease Prediction: A Machine Learning Approach by Gregorius Airlangga

“…Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate predictive models for early diagnosis and intervention. This study investigates the effectiveness of ensemble learning approaches, particularly Voting and Stacking classifiers, in comparison to traditional machine learning models and deep learning architectures. …”
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Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms by Zhong-Han Zhuang, Hui-Ping Tsai, Chung-I Chen

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Predicting Heat Treatment Duration for Pest Control Using Machine Learning on a Large-Scale Dataset by Stavros Rossos, Paraskevi Agrafioti, Vasilis Sotiroudas, Christos G. Athanassiou, Efstathios Kaloudis

“…XGBoost and random forest models outperformed others, achieving high predictive accuracy. …”
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Analysis and Selection of Multiple Machine Learning Methodologies in PyCaret for Monthly Electricity Consumption Demand Forecasting by José Orlando Quintana Quispe, Alberto Cristobal Flores Quispe, Nilton Cesar León Calvo, Osmar Cuentas Toledo

“…Forecasts for 2018 were compared with actual data, confirming the high accuracy of these models. These findings provide a robust energy management and planning framework, highlighting the potential of machine learning methodologies to optimize electricity consumption forecasting.…”
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Document Relevance Filtering by Natural Language Processing and Machine Learning: A Multidisciplinary Case Study of Patents by Raj Bridgelall

“…The contributions include benchmarking the performance of 10 classical models. These models include extreme gradient boosting, random forest, and support vector machines; a deep artificial neural network; and three natural language processing methods: latent Dirichlet allocation, non-negative matrix factorization, and k-means clustering of a manifold-learned reduced feature dimension. …”
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Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions by Hari Singh, Dola Sundeep, C. Chandrasekhara Sastry, Eswaramoorthy K Varadharaj

“…To enhance predictive accuracy, machine learning models Linear Regression, Random Forest Regression, Support Vector Machines (SVM), K-Means Clustering, and Artificial Neural Networks (ANN) were employed to analyze combustion-induced degradation trends, confirming Test-06 as the optimal balance of stability and high performance. …”
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Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning by Xiaoyan Cheng, Yao Zhou, Zhengyang Huo, Ruiying Li, Shiqian Xu, Hao Qi, Jianyuan Zhu, Fei Wang, Yang Bi

“…These findings highlight the potential of integrating color-based assessments and machine learning models into postharvest monitoring, offering a practical approach for improving quality control and storage management in the grape industry.…”
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Effective Machine Learning Techniques for Non-English Radiology Report Classification: A Danish Case Study by Alice Schiavone, Lea Marie Pehrson, Silvia Ingala, Rasmus Bonnevie, Marco Fraccaro, Dana Li, Michael Bachmann Nielsen, Desmond Elliott

“…Background: Machine learning methods for clinical assistance require a large number of annotations from trained experts to achieve optimal performance. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…Machine learning models are trained and evaluated to predict carbonate rock properties. …”
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Leveraging computational linguistics and machine learning for detection of ultra-high risk of mental health disorders in youths by Jordon Junyang Kho, Shangzheng Song, Samuel Ming Xuan Tan, Nur Hikmah Fitriyah, Matheus Calvin Lokadjaja, Jie Yin Yee, Zixu Yang, Eric Yu Hai Chen, Jimmy Lee, Wilson Wen Bin Goh

“…Regression analysis indicated UHR speech is characterized by diminished sentiment variability (β = –0.07), deviation from linguistic registers (β = –0.16), fewer phonographic neighbors (β = –0.11), lower morphological complexity (β = –0.36), and more predictable lexical structures (β = 0.05). Optimized machine learning (ML) models trained on Boruta-selected features achieved a mean AUC of 0.70. …”
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Intelligent Cyber-Physical Monitoring and Control of I4.0 Machining Systems - An Overview and Future Perspectives by Mahmoud Hassan, Ahmad Sadek, M. Helmi Attia

“…Cyber-physical adaptive control approaches have been developed to optimize the cycle time and cost while eliminating machined part defects. …”
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Unmanned Aerial Vehicle-Based Hyperspectral Imaging for Potato Virus Y Detection: Machine Learning Insights by Siddat B. Nesar, Paul W. Nugent, Nina K. Zidack, Bradley M. Whitaker

“…We used a 400–1000 nm visible and near-infrared (Vis-NIR) hyperspectral camera and trained several standard machine learning and deep learning models with optimized hyperparameters on a curated dataset. …”
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Optimising 3D-printed carbon fibre composites using machine learning: Balancing strength and efficiency by José Humberto S. Almeida, Jr., Guilherme Ferreira Gomes

“…Among the seven ML models evaluated, artificial neural networks (ANNs) achieved the highest predictive accuracy (9.2% for strength, 14.7% for time). …”
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Optimizing Imbalanced Data Classification: Under Sampling Algorithm Strategy with Classification Combination by Nauval Dwi Primadya, Adhitya Nugraha, Sahrul Yudha Fahrezi, Ardytha Luthfiarta

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Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning by Christos Stefanis, Ioannis Manisalidis, Elisavet Stavropoulou, Agathangelos Stavropoulos, Christina Tsigalou, Chrysoula (Chrysa) Voidarou, Theodoros C. Constantinidis, Eugenia Bezirtzoglou

“…These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution.…”
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Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management by Yasir Yasin, Muhammad Riaz, Kholood Alsager

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Applying machine learning to decode built environment thresholds for public and active transport distances in the global south by Ali Shkera, Domokos Esztergár-Kiss

“…By using gradient boosting decision trees, alongside six other machine learning (ML) models, including support vector machines, random forest, and artificial neural networks, this study identifies key BE factors that shape trip distances. …”
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Applications of Machine Learning in Human Factors and Ergonomics: A Comprehensive Review of Research From the Past Decade by Erman Cakit, Waldemar Karwowski

“…KNN, when optimized for k-values, yielded accuracy rates exceeding 90% in ergonomic applications. …”
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Artificial intelligence and machine learning approaches in cerebral palsy diagnosis, prognosis, and management: a comprehensive review by Shalini Dhananjay Balgude, Shilpa Gite, Biswajeet Pradhan, Chang-Wook Lee

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